Project: Identify Customer Segments¶

In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.

This notebook will help you complete this task by providing a framework within which you will perform your analysis steps. In each step of the project, you will see some text describing the subtask that you will perform, followed by one or more code cells for you to complete your work. Feel free to add additional code and markdown cells as you go along so that you can explore everything in precise chunks. The code cells provided in the base template will outline only the major tasks, and will usually not be enough to cover all of the minor tasks that comprise it.

It should be noted that while there will be precise guidelines on how you should handle certain tasks in the project, there will also be places where an exact specification is not provided. There will be times in the project where you will need to make and justify your own decisions on how to treat the data. These are places where there may not be only one way to handle the data. In real-life tasks, there may be many valid ways to approach an analysis task. One of the most important things you can do is clearly document your approach so that other scientists can understand the decisions you've made.

At the end of most sections, there will be a Markdown cell labeled Discussion. In these cells, you will report your findings for the completed section, as well as document the decisions that you made in your approach to each subtask. Your project will be evaluated not just on the code used to complete the tasks outlined, but also your communication about your observations and conclusions at each stage.

In [64]:
# import libraries here; add more as necessary
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import random

import sklearn
from sklearn.preprocessing import OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.cluster import KMeans

# magic word for producing visualizations in notebook
%matplotlib inline

pd.options.display.max_rows = None
pd.options.display.max_columns = None

Step 0: Load the Data¶

There are four files associated with this project (not including this one):

  • Udacity_AZDIAS_Subset.csv: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).
  • Udacity_CUSTOMERS_Subset.csv: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).
  • Data_Dictionary.md: Detailed information file about the features in the provided datasets.
  • AZDIAS_Feature_Summary.csv: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns

Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.

To start off with, load in the demographics data for the general population into a pandas DataFrame, and do the same for the feature attributes summary. Note for all of the .csv data files in this project: they're semicolon (;) delimited, so you'll need an additional argument in your read_csv() call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.

Once the dataset is loaded, it's recommended that you take a little bit of time just browsing the general structure of the dataset and feature summary file. You'll be getting deep into the innards of the cleaning in the first major step of the project, so gaining some general familiarity can help you get your bearings.

In [2]:
# Load in the general demographics data.
population = pd.read_csv('Udacity_AZDIAS_Subset.csv', delimiter = ';')

# Load in the feature summary file.
feat_summ = pd.read_csv('AZDIAS_Feature_Summary.csv', delimiter = ';')
In [3]:
# Check the structure of the data after it's loaded (e.g. print the number of
# rows and columns, print the first few rows).

population.head()
Out[3]:
AGER_TYP ALTERSKATEGORIE_GROB ANREDE_KZ CJT_GESAMTTYP FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER FINANZTYP GEBURTSJAHR GFK_URLAUBERTYP GREEN_AVANTGARDE HEALTH_TYP LP_LEBENSPHASE_FEIN LP_LEBENSPHASE_GROB LP_FAMILIE_FEIN LP_FAMILIE_GROB LP_STATUS_FEIN LP_STATUS_GROB NATIONALITAET_KZ PRAEGENDE_JUGENDJAHRE RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SHOPPER_TYP SOHO_KZ TITEL_KZ VERS_TYP ZABEOTYP ALTER_HH ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE KK_KUNDENTYP W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL GEBAEUDETYP KONSUMNAEHE MIN_GEBAEUDEJAHR OST_WEST_KZ WOHNLAGE CAMEO_DEUG_2015 CAMEO_DEU_2015 CAMEO_INTL_2015 KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_BAUMAX KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB
0 -1 2 1 2.0 3 4 3 5 5 3 4 0 10.0 0 -1 15.0 4.0 2.0 2.0 1.0 1.0 0 0 5.0 2 6 7 5 1 5 3 3 4 7 6 6 5 3 -1 NaN NaN -1 3 NaN NaN NaN 2.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 -1 1 2 5.0 1 5 2 5 4 5 1 1996 10.0 0 3 21.0 6.0 5.0 3.0 2.0 1.0 1 14 1.0 5 4 4 3 1 2 2 3 6 4 7 4 7 6 3 1.0 0.0 2 5 0.0 2.0 0.0 6.0 NaN 3.0 9.0 11.0 0.0 8.0 1.0 1992.0 W 4.0 8 8A 51 0.0 0.0 0.0 2.0 5.0 1.0 6.0 3.0 8.0 3.0 2.0 1.0 3.0 3.0 963.0 2.0 3.0 2.0 1.0 1.0 5.0 4.0 3.0 5.0 4.0
2 -1 3 2 3.0 1 4 1 2 3 5 1 1979 10.0 1 3 3.0 1.0 1.0 1.0 3.0 2.0 1 15 3.0 4 1 3 3 4 4 6 3 4 7 7 7 3 3 2 0.0 0.0 1 5 17.0 1.0 0.0 4.0 NaN 3.0 9.0 10.0 0.0 1.0 5.0 1992.0 W 2.0 4 4C 24 1.0 3.0 1.0 0.0 0.0 3.0 2.0 4.0 4.0 4.0 2.0 3.0 2.0 2.0 712.0 3.0 3.0 1.0 0.0 1.0 4.0 4.0 3.0 5.0 2.0
3 2 4 2 2.0 4 2 5 2 1 2 6 1957 1.0 0 2 0.0 0.0 0.0 0.0 9.0 4.0 1 8 2.0 5 1 2 1 4 4 7 4 3 4 4 5 4 4 1 0.0 0.0 1 3 13.0 0.0 0.0 1.0 NaN NaN 9.0 1.0 0.0 1.0 4.0 1997.0 W 7.0 2 2A 12 4.0 1.0 0.0 0.0 1.0 4.0 4.0 2.0 6.0 4.0 0.0 4.0 1.0 0.0 596.0 2.0 2.0 2.0 0.0 1.0 3.0 4.0 2.0 3.0 3.0
4 -1 3 1 5.0 4 3 4 1 3 2 5 1963 5.0 0 3 32.0 10.0 10.0 5.0 3.0 2.0 1 8 5.0 6 4 4 2 7 4 4 6 2 3 2 2 4 2 2 0.0 0.0 2 4 20.0 4.0 0.0 5.0 1.0 2.0 9.0 3.0 0.0 1.0 4.0 1992.0 W 3.0 6 6B 43 1.0 4.0 1.0 0.0 0.0 3.0 2.0 5.0 1.0 5.0 3.0 3.0 5.0 5.0 435.0 2.0 4.0 2.0 1.0 2.0 3.0 3.0 4.0 6.0 5.0
In [4]:
#check the shape of the data
population.shape
Out[4]:
(891221, 85)
In [5]:
#check the feature summary data 
feat_summ
Out[5]:
attribute information_level type missing_or_unknown
0 AGER_TYP person categorical [-1,0]
1 ALTERSKATEGORIE_GROB person ordinal [-1,0,9]
2 ANREDE_KZ person categorical [-1,0]
3 CJT_GESAMTTYP person categorical [0]
4 FINANZ_MINIMALIST person ordinal [-1]
5 FINANZ_SPARER person ordinal [-1]
6 FINANZ_VORSORGER person ordinal [-1]
7 FINANZ_ANLEGER person ordinal [-1]
8 FINANZ_UNAUFFAELLIGER person ordinal [-1]
9 FINANZ_HAUSBAUER person ordinal [-1]
10 FINANZTYP person categorical [-1]
11 GEBURTSJAHR person numeric [0]
12 GFK_URLAUBERTYP person categorical []
13 GREEN_AVANTGARDE person categorical []
14 HEALTH_TYP person ordinal [-1,0]
15 LP_LEBENSPHASE_FEIN person mixed [0]
16 LP_LEBENSPHASE_GROB person mixed [0]
17 LP_FAMILIE_FEIN person categorical [0]
18 LP_FAMILIE_GROB person categorical [0]
19 LP_STATUS_FEIN person categorical [0]
20 LP_STATUS_GROB person categorical [0]
21 NATIONALITAET_KZ person categorical [-1,0]
22 PRAEGENDE_JUGENDJAHRE person mixed [-1,0]
23 RETOURTYP_BK_S person ordinal [0]
24 SEMIO_SOZ person ordinal [-1,9]
25 SEMIO_FAM person ordinal [-1,9]
26 SEMIO_REL person ordinal [-1,9]
27 SEMIO_MAT person ordinal [-1,9]
28 SEMIO_VERT person ordinal [-1,9]
29 SEMIO_LUST person ordinal [-1,9]
30 SEMIO_ERL person ordinal [-1,9]
31 SEMIO_KULT person ordinal [-1,9]
32 SEMIO_RAT person ordinal [-1,9]
33 SEMIO_KRIT person ordinal [-1,9]
34 SEMIO_DOM person ordinal [-1,9]
35 SEMIO_KAEM person ordinal [-1,9]
36 SEMIO_PFLICHT person ordinal [-1,9]
37 SEMIO_TRADV person ordinal [-1,9]
38 SHOPPER_TYP person categorical [-1]
39 SOHO_KZ person categorical [-1]
40 TITEL_KZ person categorical [-1,0]
41 VERS_TYP person categorical [-1]
42 ZABEOTYP person categorical [-1,9]
43 ALTER_HH household interval [0]
44 ANZ_PERSONEN household numeric []
45 ANZ_TITEL household numeric []
46 HH_EINKOMMEN_SCORE household ordinal [-1,0]
47 KK_KUNDENTYP household categorical [-1]
48 W_KEIT_KIND_HH household ordinal [-1,0]
49 WOHNDAUER_2008 household ordinal [-1,0]
50 ANZ_HAUSHALTE_AKTIV building numeric [0]
51 ANZ_HH_TITEL building numeric []
52 GEBAEUDETYP building categorical [-1,0]
53 KONSUMNAEHE building ordinal []
54 MIN_GEBAEUDEJAHR building numeric [0]
55 OST_WEST_KZ building categorical [-1]
56 WOHNLAGE building mixed [-1]
57 CAMEO_DEUG_2015 microcell_rr4 categorical [-1,X]
58 CAMEO_DEU_2015 microcell_rr4 categorical [XX]
59 CAMEO_INTL_2015 microcell_rr4 mixed [-1,XX]
60 KBA05_ANTG1 microcell_rr3 ordinal [-1]
61 KBA05_ANTG2 microcell_rr3 ordinal [-1]
62 KBA05_ANTG3 microcell_rr3 ordinal [-1]
63 KBA05_ANTG4 microcell_rr3 ordinal [-1]
64 KBA05_BAUMAX microcell_rr3 mixed [-1,0]
65 KBA05_GBZ microcell_rr3 ordinal [-1,0]
66 BALLRAUM postcode ordinal [-1]
67 EWDICHTE postcode ordinal [-1]
68 INNENSTADT postcode ordinal [-1]
69 GEBAEUDETYP_RASTER region_rr1 ordinal []
70 KKK region_rr1 ordinal [-1,0]
71 MOBI_REGIO region_rr1 ordinal []
72 ONLINE_AFFINITAET region_rr1 ordinal []
73 REGIOTYP region_rr1 ordinal [-1,0]
74 KBA13_ANZAHL_PKW macrocell_plz8 numeric []
75 PLZ8_ANTG1 macrocell_plz8 ordinal [-1]
76 PLZ8_ANTG2 macrocell_plz8 ordinal [-1]
77 PLZ8_ANTG3 macrocell_plz8 ordinal [-1]
78 PLZ8_ANTG4 macrocell_plz8 ordinal [-1]
79 PLZ8_BAUMAX macrocell_plz8 mixed [-1,0]
80 PLZ8_HHZ macrocell_plz8 ordinal [-1]
81 PLZ8_GBZ macrocell_plz8 ordinal [-1]
82 ARBEIT community ordinal [-1,9]
83 ORTSGR_KLS9 community ordinal [-1,0]
84 RELAT_AB community ordinal [-1,9]

Tip: Add additional cells to keep everything in reasonably-sized chunks! Keyboard shortcut esc --> a (press escape to enter command mode, then press the 'A' key) adds a new cell before the active cell, and esc --> b adds a new cell after the active cell. If you need to convert an active cell to a markdown cell, use esc --> m and to convert to a code cell, use esc --> y.

Step 1: Preprocessing¶

Step 1.1: Assess Missing Data¶

The feature summary file contains a summary of properties for each demographics data column. You will use this file to help you make cleaning decisions during this stage of the project. First of all, you should assess the demographics data in terms of missing data. Pay attention to the following points as you perform your analysis, and take notes on what you observe. Make sure that you fill in the Discussion cell with your findings and decisions at the end of each step that has one!

Step 1.1.1: Convert Missing Value Codes to NaNs¶

The fourth column of the feature attributes summary (loaded in above as feat_info) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. [-1,0]), this will get read in as a string object. You'll need to do a little bit of parsing to make use of it to identify and clean the data. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.

As one more reminder, you are encouraged to add additional cells to break up your analysis into manageable chunks.

In [6]:
# Identify missing or unknown data values and convert them to NaNs.
population_missing_data = population.copy()
In [7]:
#create a function to convert the strings in missing column to list
In [8]:
def replace_missing(data,feature_summary,col_with_missing_info):
    """
    Function takes in three arguments:
    data: The dataframe in which we want to replace missing values with Nan
    feature_summary: The dataframe which has information on the columns and the missing values in the column 
    col_with_missing_info: the name of the column in feature_summary that has the missing value info
    """
    for attribute in feature_summary['attribute']:
        missing_list = feature_summary[feature_summary['attribute']==attribute][col_with_missing_info].iloc[0].replace('[','').replace(']','').split(',')
        missing_list = [int(val) if val.lstrip('-').isnumeric() else val for val in missing_list]
    
        data[attribute].replace(to_replace=missing_list,value=np.nan, inplace=True)
    
    return data
In [9]:
#use the function to replace the missing values
population_replaced = replace_missing(population_missing_data,feat_summ,col_with_missing_info='missing_or_unknown')
population_replaced.head()
Out[9]:
AGER_TYP ALTERSKATEGORIE_GROB ANREDE_KZ CJT_GESAMTTYP FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER FINANZTYP GEBURTSJAHR GFK_URLAUBERTYP GREEN_AVANTGARDE HEALTH_TYP LP_LEBENSPHASE_FEIN LP_LEBENSPHASE_GROB LP_FAMILIE_FEIN LP_FAMILIE_GROB LP_STATUS_FEIN LP_STATUS_GROB NATIONALITAET_KZ PRAEGENDE_JUGENDJAHRE RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SHOPPER_TYP SOHO_KZ TITEL_KZ VERS_TYP ZABEOTYP ALTER_HH ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE KK_KUNDENTYP W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL GEBAEUDETYP KONSUMNAEHE MIN_GEBAEUDEJAHR OST_WEST_KZ WOHNLAGE CAMEO_DEUG_2015 CAMEO_DEU_2015 CAMEO_INTL_2015 KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_BAUMAX KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB
0 NaN 2.0 1 2.0 3 4 3 5 5 3 4 NaN 10.0 0 NaN 15.0 4.0 2.0 2.0 1.0 1.0 NaN NaN 5.0 2 6 7 5 1 5 3 3 4 7 6 6 5 3 NaN NaN NaN NaN 3 NaN NaN NaN 2.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 NaN 1.0 2 5.0 1 5 2 5 4 5 1 1996.0 10.0 0 3.0 21.0 6.0 5.0 3.0 2.0 1.0 1.0 14.0 1.0 5 4 4 3 1 2 2 3 6 4 7 4 7 6 3.0 1.0 NaN 2.0 5 NaN 2.0 0.0 6.0 NaN 3.0 9.0 11.0 0.0 8.0 1.0 1992.0 W 4.0 8 8A 51 0.0 0.0 0.0 2.0 5.0 1.0 6.0 3.0 8.0 3.0 2.0 1.0 3.0 3.0 963.0 2.0 3.0 2.0 1.0 1.0 5.0 4.0 3.0 5.0 4.0
2 NaN 3.0 2 3.0 1 4 1 2 3 5 1 1979.0 10.0 1 3.0 3.0 1.0 1.0 1.0 3.0 2.0 1.0 15.0 3.0 4 1 3 3 4 4 6 3 4 7 7 7 3 3 2.0 0.0 NaN 1.0 5 17.0 1.0 0.0 4.0 NaN 3.0 9.0 10.0 0.0 1.0 5.0 1992.0 W 2.0 4 4C 24 1.0 3.0 1.0 0.0 NaN 3.0 2.0 4.0 4.0 4.0 2.0 3.0 2.0 2.0 712.0 3.0 3.0 1.0 0.0 1.0 4.0 4.0 3.0 5.0 2.0
3 2.0 4.0 2 2.0 4 2 5 2 1 2 6 1957.0 1.0 0 2.0 NaN NaN NaN NaN 9.0 4.0 1.0 8.0 2.0 5 1 2 1 4 4 7 4 3 4 4 5 4 4 1.0 0.0 NaN 1.0 3 13.0 0.0 0.0 1.0 NaN NaN 9.0 1.0 0.0 1.0 4.0 1997.0 W 7.0 2 2A 12 4.0 1.0 0.0 0.0 1.0 4.0 4.0 2.0 6.0 4.0 NaN 4.0 1.0 NaN 596.0 2.0 2.0 2.0 0.0 1.0 3.0 4.0 2.0 3.0 3.0
4 NaN 3.0 1 5.0 4 3 4 1 3 2 5 1963.0 5.0 0 3.0 32.0 10.0 10.0 5.0 3.0 2.0 1.0 8.0 5.0 6 4 4 2 7 4 4 6 2 3 2 2 4 2 2.0 0.0 NaN 2.0 4 20.0 4.0 0.0 5.0 1.0 2.0 9.0 3.0 0.0 1.0 4.0 1992.0 W 3.0 6 6B 43 1.0 4.0 1.0 0.0 NaN 3.0 2.0 5.0 1.0 5.0 3.0 3.0 5.0 5.0 435.0 2.0 4.0 2.0 1.0 2.0 3.0 3.0 4.0 6.0 5.0

Step 1.1.2: Assess Missing Data in Each Column¶

How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's hist() function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)

For the remaining features, are there any patterns in which columns have, or share, missing data?

In [10]:
# Perform an assessment of how much missing data there is in each column of the
# dataset.
nulls = population_replaced.isnull().sum().sort_values(ascending=False)
nulls
Out[10]:
TITEL_KZ                 889061
AGER_TYP                 685843
KK_KUNDENTYP             584612
KBA05_BAUMAX             476524
GEBURTSJAHR              392318
ALTER_HH                 310267
KKK                      158064
REGIOTYP                 158064
W_KEIT_KIND_HH           147988
KBA05_ANTG1              133324
KBA05_ANTG2              133324
KBA05_ANTG3              133324
KBA05_ANTG4              133324
KBA05_GBZ                133324
MOBI_REGIO               133324
PLZ8_ANTG3               116515
PLZ8_ANTG2               116515
PLZ8_GBZ                 116515
PLZ8_HHZ                 116515
PLZ8_ANTG1               116515
PLZ8_BAUMAX              116515
PLZ8_ANTG4               116515
VERS_TYP                 111196
HEALTH_TYP               111196
SHOPPER_TYP              111196
NATIONALITAET_KZ         108315
PRAEGENDE_JUGENDJAHRE    108164
KBA13_ANZAHL_PKW         105800
ANZ_HAUSHALTE_AKTIV       99611
CAMEO_INTL_2015           99352
CAMEO_DEU_2015            99352
CAMEO_DEUG_2015           99352
LP_LEBENSPHASE_FEIN       97632
ARBEIT                    97375
RELAT_AB                  97375
ORTSGR_KLS9               97274
ANZ_HH_TITEL              97008
LP_LEBENSPHASE_GROB       94572
INNENSTADT                93740
EWDICHTE                  93740
BALLRAUM                  93740
GEBAEUDETYP_RASTER        93155
WOHNLAGE                  93148
OST_WEST_KZ               93148
MIN_GEBAEUDEJAHR          93148
GEBAEUDETYP               93148
LP_FAMILIE_FEIN           77792
LP_FAMILIE_GROB           77792
KONSUMNAEHE               73969
WOHNDAUER_2008            73499
SOHO_KZ                   73499
ANZ_TITEL                 73499
ANZ_PERSONEN              73499
HH_EINKOMMEN_SCORE        18348
GFK_URLAUBERTYP            4854
CJT_GESAMTTYP              4854
LP_STATUS_FEIN             4854
LP_STATUS_GROB             4854
RETOURTYP_BK_S             4854
ONLINE_AFFINITAET          4854
ALTERSKATEGORIE_GROB       2881
FINANZ_UNAUFFAELLIGER         0
FINANZTYP                     0
FINANZ_HAUSBAUER              0
GREEN_AVANTGARDE              0
FINANZ_SPARER                 0
FINANZ_MINIMALIST             0
FINANZ_VORSORGER              0
FINANZ_ANLEGER                0
ANREDE_KZ                     0
SEMIO_KAEM                    0
SEMIO_SOZ                     0
SEMIO_PFLICHT                 0
SEMIO_FAM                     0
SEMIO_REL                     0
SEMIO_MAT                     0
SEMIO_VERT                    0
SEMIO_LUST                    0
SEMIO_ERL                     0
SEMIO_KULT                    0
SEMIO_RAT                     0
SEMIO_KRIT                    0
SEMIO_DOM                     0
SEMIO_TRADV                   0
ZABEOTYP                      0
dtype: int64
In [11]:
#check the proportion of values that are null in each column
nulls_proportion = (nulls/population_replaced.shape[0])
nulls_proportion
Out[11]:
TITEL_KZ                 0.997576
AGER_TYP                 0.769554
KK_KUNDENTYP             0.655967
KBA05_BAUMAX             0.534687
GEBURTSJAHR              0.440203
ALTER_HH                 0.348137
KKK                      0.177357
REGIOTYP                 0.177357
W_KEIT_KIND_HH           0.166051
KBA05_ANTG1              0.149597
KBA05_ANTG2              0.149597
KBA05_ANTG3              0.149597
KBA05_ANTG4              0.149597
KBA05_GBZ                0.149597
MOBI_REGIO               0.149597
PLZ8_ANTG3               0.130736
PLZ8_ANTG2               0.130736
PLZ8_GBZ                 0.130736
PLZ8_HHZ                 0.130736
PLZ8_ANTG1               0.130736
PLZ8_BAUMAX              0.130736
PLZ8_ANTG4               0.130736
VERS_TYP                 0.124768
HEALTH_TYP               0.124768
SHOPPER_TYP              0.124768
NATIONALITAET_KZ         0.121536
PRAEGENDE_JUGENDJAHRE    0.121366
KBA13_ANZAHL_PKW         0.118714
ANZ_HAUSHALTE_AKTIV      0.111769
CAMEO_INTL_2015          0.111479
CAMEO_DEU_2015           0.111479
CAMEO_DEUG_2015          0.111479
LP_LEBENSPHASE_FEIN      0.109549
ARBEIT                   0.109260
RELAT_AB                 0.109260
ORTSGR_KLS9              0.109147
ANZ_HH_TITEL             0.108848
LP_LEBENSPHASE_GROB      0.106115
INNENSTADT               0.105182
EWDICHTE                 0.105182
BALLRAUM                 0.105182
GEBAEUDETYP_RASTER       0.104525
WOHNLAGE                 0.104517
OST_WEST_KZ              0.104517
MIN_GEBAEUDEJAHR         0.104517
GEBAEUDETYP              0.104517
LP_FAMILIE_FEIN          0.087287
LP_FAMILIE_GROB          0.087287
KONSUMNAEHE              0.082997
WOHNDAUER_2008           0.082470
SOHO_KZ                  0.082470
ANZ_TITEL                0.082470
ANZ_PERSONEN             0.082470
HH_EINKOMMEN_SCORE       0.020587
GFK_URLAUBERTYP          0.005446
CJT_GESAMTTYP            0.005446
LP_STATUS_FEIN           0.005446
LP_STATUS_GROB           0.005446
RETOURTYP_BK_S           0.005446
ONLINE_AFFINITAET        0.005446
ALTERSKATEGORIE_GROB     0.003233
FINANZ_UNAUFFAELLIGER    0.000000
FINANZTYP                0.000000
FINANZ_HAUSBAUER         0.000000
GREEN_AVANTGARDE         0.000000
FINANZ_SPARER            0.000000
FINANZ_MINIMALIST        0.000000
FINANZ_VORSORGER         0.000000
FINANZ_ANLEGER           0.000000
ANREDE_KZ                0.000000
SEMIO_KAEM               0.000000
SEMIO_SOZ                0.000000
SEMIO_PFLICHT            0.000000
SEMIO_FAM                0.000000
SEMIO_REL                0.000000
SEMIO_MAT                0.000000
SEMIO_VERT               0.000000
SEMIO_LUST               0.000000
SEMIO_ERL                0.000000
SEMIO_KULT               0.000000
SEMIO_RAT                0.000000
SEMIO_KRIT               0.000000
SEMIO_DOM                0.000000
SEMIO_TRADV              0.000000
ZABEOTYP                 0.000000
dtype: float64
In [12]:
# Investigate patterns in the amount of missing data in each column.
nulls.hist()
Out[12]:
<Axes: >
No description has been provided for this image
In [13]:
# Remove the outlier columns from the dataset. (You'll perform other data
# engineering tasks such as re-encoding and imputation later.)
In [14]:
#lets remove the columns that have more than 30% as null values 
cols_to_drop=list((nulls_proportion[nulls_proportion >= 0.3]).index)
population_replaced.drop(cols_to_drop, axis='columns', inplace=True)
In [15]:
#also drop the same cols from feat_summ
feat_new = feat_summ[~feat_summ['attribute'].isin(cols_to_drop)]
feat_new.shape
Out[15]:
(79, 4)
In [16]:
population_replaced.shape
Out[16]:
(891221, 79)

Discussion 1.1.2: Assess Missing Data in Each Column¶

(Double click this cell and replace this text with your own text, reporting your observations regarding the amount of missing data in each column. Are there any patterns in missing values? Which columns were removed from the dataset?)

  • Some columns have no missing data, while others contain a significant amount of missing data. The 'TITEL_KZ' column stands out with over 99% of its data missing.
  • The 3 columns with the most missing data have categorical features
  • 6 columns (TITEL_KZ, AGER_TYP, KK_KUNDENTYP, KBA05_BAUMAX, GEBURTSJAHR, ALTER_HH)have more than 30% of the data missing. These columns have been dropped

Step 1.1.3: Assess Missing Data in Each Row¶

Now, you'll perform a similar assessment for the rows of the dataset. How much data is missing in each row? As with the columns, you should see some groups of points that have a very different numbers of missing values. Divide the data into two subsets: one for data points that are above some threshold for missing values, and a second subset for points below that threshold.

In order to know what to do with the outlier rows, we should see if the distribution of data values on columns that are not missing data (or are missing very little data) are similar or different between the two groups. Select at least five of these columns and compare the distribution of values.

  • You can use seaborn's countplot() function to create a bar chart of code frequencies and matplotlib's subplot() function to put bar charts for the two subplots side by side.
  • To reduce repeated code, you might want to write a function that can perform this comparison, taking as one of its arguments a column to be compared.

Depending on what you observe in your comparison, this will have implications on how you approach your conclusions later in the analysis. If the distributions of non-missing features look similar between the data with many missing values and the data with few or no missing values, then we could argue that simply dropping those points from the analysis won't present a major issue. On the other hand, if the data with many missing values looks very different from the data with few or no missing values, then we should make a note on those data as special. We'll revisit these data later on. Either way, you should continue your analysis for now using just the subset of the data with few or no missing values.

In [17]:
# How much data is missing in each row of the dataset?
population_replaced.isnull().sum(axis=1).sort_values(ascending=False).hist()
Out[17]:
<Axes: >
No description has been provided for this image
In [18]:
# Write code to divide the data into two subsets based on the number of missing
# values in each row.
In [19]:
#lets follow the same 30% rule we used for the columns and set our treshold as 24 (30% of the number of columns)
below_thresh = population_replaced.drop(population_replaced[population_replaced.isnull().sum(axis=1)>24].index)
above_thresh = population_replaced.drop(population_replaced[population_replaced.isnull().sum(axis=1)<=24].index)
In [20]:
# Compare the distribution of values for at least five columns where there are
# no or few missing values, between the two subsets.
In [21]:
#names of cols with no missing data in the below thresh group
cols_below_thresh = list(below_thresh.isnull().sum()[below_thresh.isnull().sum()==0].index)

#names of cols with no missing data in the above thresh group
cols_above_thresh = list(above_thresh.isnull().sum()[above_thresh.isnull().sum()==0].index)
In [22]:
#randomly select 5 cols that are present in both below_thresh & above_thresh

common_columns = list(set(cols_below_thresh) & set(cols_above_thresh))

random.seed(0) 
selected_columns = random.sample(common_columns, 5)
selected_columns
Out[22]:
['FINANZTYP',
 'GREEN_AVANTGARDE',
 'SEMIO_REL',
 'SEMIO_DOM',
 'FINANZ_MINIMALIST']
In [23]:
for col in selected_columns:

    plt.figure(figsize=(10, 4))
    ax1 = plt.subplot(1,2,1)
    sns.countplot(data = above_thresh, x = col, ax=ax1)
    ax1.set_title(f'Distribution of above_thresh - {col}')

    ax2 = plt.subplot(1,2,2)
    sns.countplot(data = below_thresh, x = col, ax=ax2)
    ax2.set_title(f'Distribution of below_thresh - {col}')
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
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No description has been provided for this image

Discussion 1.1.3: Assess Missing Data in Each Row¶

(Double-click this cell and replace this text with your own text, reporting your observations regarding missing data in rows. Are the data with lots of missing values are qualitatively different from data with few or no missing values?)

The data with lots of missing values (above_threshold of 24 columns with missing data) has a different distribution with data with less than 24 missing columns.

Step 1.2: Select and Re-Encode Features¶

Checking for missing data isn't the only way in which you can prepare a dataset for analysis. Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, you need to make a few encoding changes or additional assumptions to be able to make progress. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (feat_info) for a summary of types of measurement.

  • For numeric and interval data, these features can be kept without changes.
  • Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).
  • Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.

In the first two parts of this sub-step, you will perform an investigation of the categorical and mixed-type features and make a decision on each of them, whether you will keep, drop, or re-encode each. Then, in the last part, you will create a new data frame with only the selected and engineered columns.

Data wrangling is often the trickiest part of the data analysis process, and there's a lot of it to be done here. But stick with it: once you're done with this step, you'll be ready to get to the machine learning parts of the project!

In [24]:
# How many features are there of each data type? (note that we have removed the 6 columns with over 30% missing values

feat_new['type'].value_counts()
Out[24]:
type
ordinal        49
categorical    18
mixed           6
numeric         6
Name: count, dtype: int64

Step 1.2.1: Re-Encode Categorical Features¶

For categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:

  • For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.
  • There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.
  • For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via OneHotEncoder), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section.
In [25]:
#let's look at the no and type of values in categorical columns 

cat = list(feat_new[feat_new['type']=='categorical']['attribute'])
cat
Out[25]:
['ANREDE_KZ',
 'CJT_GESAMTTYP',
 'FINANZTYP',
 'GFK_URLAUBERTYP',
 'GREEN_AVANTGARDE',
 'LP_FAMILIE_FEIN',
 'LP_FAMILIE_GROB',
 'LP_STATUS_FEIN',
 'LP_STATUS_GROB',
 'NATIONALITAET_KZ',
 'SHOPPER_TYP',
 'SOHO_KZ',
 'VERS_TYP',
 'ZABEOTYP',
 'GEBAEUDETYP',
 'OST_WEST_KZ',
 'CAMEO_DEUG_2015',
 'CAMEO_DEU_2015']

We will continue analysis here with the rows that have less than 24 missing columns (below_thresh)

In [26]:
for col in cat:
    print(f'{col} has {below_thresh[col].nunique()} unique values. They are {below_thresh[col].unique()}')
    print('\n ................................................................... \n')

ANREDE_KZ has 2 unique values. They are [2 1]

 ................................................................... 

CJT_GESAMTTYP has 6 unique values. They are [ 5.  3.  2.  4.  1.  6. nan]

 ................................................................... 

FINANZTYP has 6 unique values. They are [1 6 5 2 4 3]

 ................................................................... 

GFK_URLAUBERTYP has 12 unique values. They are [10.  1.  5. 12.  9.  3.  8. 11.  4.  2.  7.  6. nan]

 ................................................................... 

GREEN_AVANTGARDE has 2 unique values. They are [0 1]

 ................................................................... 

LP_FAMILIE_FEIN has 11 unique values. They are [ 5.  1. nan 10.  2.  7. 11.  8.  4.  6.  9.  3.]

 ................................................................... 

LP_FAMILIE_GROB has 5 unique values. They are [ 3.  1. nan  5.  2.  4.]

 ................................................................... 

LP_STATUS_FEIN has 10 unique values. They are [ 2.  3.  9.  4.  1. 10.  5.  8.  6.  7. nan]

 ................................................................... 

LP_STATUS_GROB has 5 unique values. They are [ 1.  2.  4.  5.  3. nan]

 ................................................................... 

NATIONALITAET_KZ has 3 unique values. They are [ 1.  3.  2. nan]

 ................................................................... 

SHOPPER_TYP has 4 unique values. They are [ 3.  2.  1.  0. nan]

 ................................................................... 

SOHO_KZ has 2 unique values. They are [1. 0.]

 ................................................................... 

VERS_TYP has 2 unique values. They are [ 2.  1. nan]

 ................................................................... 

ZABEOTYP has 6 unique values. They are [5 3 4 1 6 2]

 ................................................................... 

GEBAEUDETYP has 7 unique values. They are [8. 1. 3. 2. 6. 4. 5.]

 ................................................................... 

OST_WEST_KZ has 2 unique values. They are ['W' 'O']

 ................................................................... 

CAMEO_DEUG_2015 has 9 unique values. They are ['8' '4' '2' '6' '1' '9' '5' '7' nan '3']

 ................................................................... 

CAMEO_DEU_2015 has 44 unique values. They are ['8A' '4C' '2A' '6B' '8C' '4A' '2D' '1A' '1E' '9D' '5C' '8B' '7A' '5D'
 '9E' nan '9B' '1B' '3D' '4E' '4B' '3C' '5A' '7B' '9A' '6D' '6E' '2C' '7C'
 '9C' '7D' '5E' '1D' '8D' '6C' '6A' '5B' '4D' '3A' '2B' '7E' '3B' '6F'
 '5F' '1C']

 ...................................................................
In [27]:
# Assess categorical variables: which are binary, which are multi-level, and
# which one needs to be re-encoded?
In [28]:
# Re-encode categorical variable(s) to be kept in the analysis.
In [29]:
# We need to re encode the values in columns ANREDE_KZ and OST_WEST_KZ to 0 and 1 
below_thresh['OST_WEST_KZ'].replace(['W', 'O'], [1, 0], inplace=True)
below_thresh['ANREDE_KZ'].replace([2, 1], [1, 0], inplace=True)
In [30]:
#we will perform one hot encoding for the multi-level features
In [31]:
#get the list of columns with more than two unique values 
multi_level = [col for col in cat if below_thresh[col].nunique() > 2]
multi_level
Out[31]:
['CJT_GESAMTTYP',
 'FINANZTYP',
 'GFK_URLAUBERTYP',
 'LP_FAMILIE_FEIN',
 'LP_FAMILIE_GROB',
 'LP_STATUS_FEIN',
 'LP_STATUS_GROB',
 'NATIONALITAET_KZ',
 'SHOPPER_TYP',
 'ZABEOTYP',
 'GEBAEUDETYP',
 'CAMEO_DEUG_2015',
 'CAMEO_DEU_2015']
In [32]:
#before one hot encoding 
below_thresh.shape[1]
Out[32]:
79
In [33]:
below_thresh = pd.get_dummies(below_thresh, columns=multi_level)
below_thresh.shape[1]
Out[33]:
194

Discussion 1.2.1: Re-Encode Categorical Features¶

(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding categorical features. Which ones did you keep, which did you drop, and what engineering steps did you perform?)

  • i chose to keep the multi-level features as these features may have information that is important for our analysis

Step 1.2.2: Engineer Mixed-Type Features¶

There are a handful of features that are marked as "mixed" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:

  • "PRAEGENDE_JUGENDJAHRE" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.
  • "CAMEO_INTL_2015" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).
  • If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.

Be sure to check Data_Dictionary.md for the details needed to finish these tasks.

In [34]:
# Investigate "PRAEGENDE_JUGENDJAHRE" and engineer two new variables.
#create a dict to store the values to be used to create new columns 
gen_mov = {1: [40,0], 2: [40,1], 3: [50,0], 4: [50,1], 5: [60,0], 6: [60,1], 7: [60,1], 8: [70,0], 9: [70,1], 10: [80,0], 11: [80,1], 12: [80,0],
13: [80,1], 14: [90,0], 15: [90,1]}

below_thresh['GENERATION'] = below_thresh['PRAEGENDE_JUGENDJAHRE'].apply(lambda val: np.nan if np.isnan(val) else gen_mov[val][0])
below_thresh['MOVEMENT'] = below_thresh['PRAEGENDE_JUGENDJAHRE'].apply(lambda val: np.nan if np.isnan(val) else gen_mov[val][1])
In [35]:
#REMOVE the PRAEGENDE_JUGENDJAHRE col
below_thresh.drop('PRAEGENDE_JUGENDJAHRE', axis=1, inplace=True)
In [36]:
#check the no of cols - should be 195
below_thresh.shape[1]
Out[36]:
195
In [37]:
# Investigate "CAMEO_INTL_2015" and engineer two new variables.
below_thresh['CAMEO_INTL_2015'].dtype
Out[37]:
dtype('O')
In [38]:
#create two new variables from the values in CAMEO_INTL_2015
below_thresh['WEALTH'] = below_thresh['CAMEO_INTL_2015'].str[0]
below_thresh['LIFE_STAGE'] = below_thresh['CAMEO_INTL_2015'].str[1]
In [39]:
#REMOVE the CAMEO_INTL_2015 col and check the no of columns
below_thresh.drop('CAMEO_INTL_2015', axis=1, inplace=True)
below_thresh.shape[1]
Out[39]:
196

Discussion 1.2.2: Engineer Mixed-Type Features¶

(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding mixed-value features. Which ones did you keep, which did you drop, and what engineering steps did you perform?)

Step 1.2.3: Complete Feature Selection¶

In order to finish this step up, you need to make sure that your data frame now only has the columns that you want to keep. To summarize, the dataframe should consist of the following:

  • All numeric, interval, and ordinal type columns from the original dataset.
  • Binary categorical features (all numerically-encoded).
  • Engineered features from other multi-level categorical features and mixed features.

Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep "PRAEGENDE_JUGENDJAHRE", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from the subset with few or no missing values.

In [40]:
# If there are other re-engineering tasks you need to perform, make sure you
# take care of them here. (Dealing with missing data will come in step 2.1.)
#drop the 2 cols that were just removed 
feat_new.drop
mixed = list(feat_new[feat_new['type'] == 'mixed']['attribute'])
mixed.remove('PRAEGENDE_JUGENDJAHRE')
mixed.remove('CAMEO_INTL_2015')
feat_new = feat_new[~feat_new['attribute'].isin(mixed)]
feat_new
Out[40]:
attribute information_level type missing_or_unknown
1 ALTERSKATEGORIE_GROB person ordinal [-1,0,9]
2 ANREDE_KZ person categorical [-1,0]
3 CJT_GESAMTTYP person categorical [0]
4 FINANZ_MINIMALIST person ordinal [-1]
5 FINANZ_SPARER person ordinal [-1]
6 FINANZ_VORSORGER person ordinal [-1]
7 FINANZ_ANLEGER person ordinal [-1]
8 FINANZ_UNAUFFAELLIGER person ordinal [-1]
9 FINANZ_HAUSBAUER person ordinal [-1]
10 FINANZTYP person categorical [-1]
12 GFK_URLAUBERTYP person categorical []
13 GREEN_AVANTGARDE person categorical []
14 HEALTH_TYP person ordinal [-1,0]
17 LP_FAMILIE_FEIN person categorical [0]
18 LP_FAMILIE_GROB person categorical [0]
19 LP_STATUS_FEIN person categorical [0]
20 LP_STATUS_GROB person categorical [0]
21 NATIONALITAET_KZ person categorical [-1,0]
22 PRAEGENDE_JUGENDJAHRE person mixed [-1,0]
23 RETOURTYP_BK_S person ordinal [0]
24 SEMIO_SOZ person ordinal [-1,9]
25 SEMIO_FAM person ordinal [-1,9]
26 SEMIO_REL person ordinal [-1,9]
27 SEMIO_MAT person ordinal [-1,9]
28 SEMIO_VERT person ordinal [-1,9]
29 SEMIO_LUST person ordinal [-1,9]
30 SEMIO_ERL person ordinal [-1,9]
31 SEMIO_KULT person ordinal [-1,9]
32 SEMIO_RAT person ordinal [-1,9]
33 SEMIO_KRIT person ordinal [-1,9]
34 SEMIO_DOM person ordinal [-1,9]
35 SEMIO_KAEM person ordinal [-1,9]
36 SEMIO_PFLICHT person ordinal [-1,9]
37 SEMIO_TRADV person ordinal [-1,9]
38 SHOPPER_TYP person categorical [-1]
39 SOHO_KZ person categorical [-1]
41 VERS_TYP person categorical [-1]
42 ZABEOTYP person categorical [-1,9]
44 ANZ_PERSONEN household numeric []
45 ANZ_TITEL household numeric []
46 HH_EINKOMMEN_SCORE household ordinal [-1,0]
48 W_KEIT_KIND_HH household ordinal [-1,0]
49 WOHNDAUER_2008 household ordinal [-1,0]
50 ANZ_HAUSHALTE_AKTIV building numeric [0]
51 ANZ_HH_TITEL building numeric []
52 GEBAEUDETYP building categorical [-1,0]
53 KONSUMNAEHE building ordinal []
54 MIN_GEBAEUDEJAHR building numeric [0]
55 OST_WEST_KZ building categorical [-1]
57 CAMEO_DEUG_2015 microcell_rr4 categorical [-1,X]
58 CAMEO_DEU_2015 microcell_rr4 categorical [XX]
59 CAMEO_INTL_2015 microcell_rr4 mixed [-1,XX]
60 KBA05_ANTG1 microcell_rr3 ordinal [-1]
61 KBA05_ANTG2 microcell_rr3 ordinal [-1]
62 KBA05_ANTG3 microcell_rr3 ordinal [-1]
63 KBA05_ANTG4 microcell_rr3 ordinal [-1]
65 KBA05_GBZ microcell_rr3 ordinal [-1,0]
66 BALLRAUM postcode ordinal [-1]
67 EWDICHTE postcode ordinal [-1]
68 INNENSTADT postcode ordinal [-1]
69 GEBAEUDETYP_RASTER region_rr1 ordinal []
70 KKK region_rr1 ordinal [-1,0]
71 MOBI_REGIO region_rr1 ordinal []
72 ONLINE_AFFINITAET region_rr1 ordinal []
73 REGIOTYP region_rr1 ordinal [-1,0]
74 KBA13_ANZAHL_PKW macrocell_plz8 numeric []
75 PLZ8_ANTG1 macrocell_plz8 ordinal [-1]
76 PLZ8_ANTG2 macrocell_plz8 ordinal [-1]
77 PLZ8_ANTG3 macrocell_plz8 ordinal [-1]
78 PLZ8_ANTG4 macrocell_plz8 ordinal [-1]
80 PLZ8_HHZ macrocell_plz8 ordinal [-1]
81 PLZ8_GBZ macrocell_plz8 ordinal [-1]
82 ARBEIT community ordinal [-1,9]
83 ORTSGR_KLS9 community ordinal [-1,0]
84 RELAT_AB community ordinal [-1,9]
In [41]:
cols_to_drop.extend(mixed)
cols_to_drop
Out[41]:
['TITEL_KZ',
 'AGER_TYP',
 'KK_KUNDENTYP',
 'KBA05_BAUMAX',
 'GEBURTSJAHR',
 'ALTER_HH',
 'LP_LEBENSPHASE_FEIN',
 'LP_LEBENSPHASE_GROB',
 'WOHNLAGE',
 'PLZ8_BAUMAX']
In [42]:
# Do whatever you need to in order to ensure that the dataframe only contains
# the columns that should be passed to the algorithm functions.

#drop all the other mixed columns 
below_thresh.drop(mixed, axis=1, inplace=True)
below_thresh.shape[1]
Out[42]:
192

Step 1.3: Create a Cleaning Function¶

Even though you've finished cleaning up the general population demographics data, it's important to look ahead to the future and realize that you'll need to perform the same cleaning steps on the customer demographics data. In this substep, complete the function below to execute the main feature selection, encoding, and re-engineering steps you performed above. Then, when it comes to looking at the customer data in Step 3, you can just run this function on that DataFrame to get the trimmed dataset in a single step.

In [133]:
def clean_data(df):
    """
    Perform feature trimming, re-encoding, and engineering for demographics
    data
    
    INPUT: Demographics DataFrame
    OUTPUT: Trimmed and cleaned demographics DataFrame
    """
    
    # Put in code here to execute all main cleaning steps:
    # convert missing value codes into NaNs, ...

    feat_summary = pd.read_csv('AZDIAS_Feature_Summary.csv', delimiter = ';')
    
    for attribute in feat_summary['attribute']:
        missing_list = feat_summary[feat_summary['attribute']==attribute]['missing_or_unknown'].iloc[0].replace('[','').replace(']','').split(',')
        missing_list = [int(val) if val.lstrip('-').isnumeric() else val for val in missing_list]
    
        df[attribute].replace(to_replace=missing_list,value=np.nan, inplace=True)
 
    
    # remove selected columns and rows, ...
    #remove columns in the cols_to_drop list 
    df.drop(cols_to_drop, axis=1, inplace=True)

    #drop rows that have a missing value in more than 24 columns 
    df = df.drop(df[df.isnull().sum(axis=1)>24].index)

    #re- encode values in the binary columns
    df['OST_WEST_KZ'].replace(['W', 'O'], [1, 0], inplace=True)
    df['ANREDE_KZ'].replace([2, 1], [1, 0], inplace=True)

    #perform one hot encoding for columns with more than 2 unique values
    df = pd.get_dummies(df, columns=multi_level, dtype=float)

    # re- engineer column values.
    df['GENERATION'] = df['PRAEGENDE_JUGENDJAHRE'].apply(lambda val: np.nan if np.isnan(val) else gen_mov[val][0])
    df['MOVEMENT'] = df['PRAEGENDE_JUGENDJAHRE'].apply(lambda val: np.nan if np.isnan(val) else gen_mov[val][1])

    df['WEALTH'] = df['CAMEO_INTL_2015'].str[0]
    df['LIFE_STAGE'] = df['CAMEO_INTL_2015'].str[1]

    #drop the two columns that were re-engineered
    df.drop(['PRAEGENDE_JUGENDJAHRE','CAMEO_INTL_2015'], axis=1, inplace=True)
    
    # Return the cleaned dataframe.
    return df

Step 2: Feature Transformation¶

Step 2.1: Apply Feature Scaling¶

Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll want to keep an eye on the API reference page for sklearn to help you navigate to all of the classes and functions that you'll need. In this substep, you'll need to check the following:

  • sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an SimpleImputer to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.
  • For the actual scaling function, a StandardScaler instance is suggested, scaling each feature to mean 0 and standard deviation 1.
  • For these classes, you can make use of the .fit_transform() method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project.
In [44]:
# If you've not yet cleaned the dataset of all NaN values, then investigate and
# do that now.
In [45]:
#lets find out how many rows in our df have at least one col with a missing value
print(f'There are {below_thresh.isnull().any(axis=1).sum()} rows with at least one missing column out of a total of {below_thresh.shape[0]} rows')

There are 174695 rows with at least one missing column out of a total of 797906 rows

This means like 25% of our data has at least one missing column. Dropping these rows may make us lose valuable info about our data. so lets use the imputer instead

In [46]:
from sklearn.impute import SimpleImputer

imputer = SimpleImputer(strategy='mean')
cleaned_data = pd.DataFrame(imputer.fit_transform(below_thresh), columns=below_thresh.columns)
In [47]:
# Apply feature scaling to the general population demographics data.
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler = scaler.fit(cleaned_data)
scaled_data = scaler.transform(cleaned_data)
scaled_df = pd.DataFrame(scaled_data, columns=cleaned_data.keys())

Discussion 2.1: Apply Feature Scaling¶

(Double-click this cell and replace this text with your own text, reporting your decisions regarding feature scaling.)

Step 2.2: Perform Dimensionality Reduction¶

On your scaled data, you are now ready to apply dimensionality reduction techniques.

  • Use sklearn's PCA class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).
  • Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's plot() function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.
  • Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation.
In [48]:
# Apply PCA to the data.
from sklearn.decomposition import PCA
pca = PCA()
data_pca = pca.fit_transform(scaled_df)
In [49]:
pca.explained_variance_ratio_
Out[49]:
array([7.91510334e-02, 5.78184209e-02, 3.56057665e-02, 2.71881133e-02,
       2.05513652e-02, 1.59813466e-02, 1.51415174e-02, 1.35439634e-02,
       1.31358770e-02, 1.26551791e-02, 1.21452876e-02, 1.19528956e-02,
       1.16956700e-02, 1.15501645e-02, 1.13613085e-02, 1.11504040e-02,
       1.09158481e-02, 1.07828234e-02, 1.06295686e-02, 1.04268820e-02,
       1.01904040e-02, 9.83463096e-03, 9.51662311e-03, 9.17315519e-03,
       8.64972453e-03, 8.49870807e-03, 8.35028207e-03, 8.06852468e-03,
       7.51236033e-03, 7.28458839e-03, 7.17259391e-03, 7.00347078e-03,
       6.81438760e-03, 6.77504642e-03, 6.69788656e-03, 6.61478027e-03,
       6.55701172e-03, 6.33600601e-03, 6.25774479e-03, 6.17747709e-03,
       6.14470781e-03, 5.94235553e-03, 5.88197659e-03, 5.82711614e-03,
       5.79788001e-03, 5.73404088e-03, 5.70229743e-03, 5.64023509e-03,
       5.60964055e-03, 5.56673976e-03, 5.55080310e-03, 5.53804249e-03,
       5.48746826e-03, 5.45587405e-03, 5.43951107e-03, 5.41544092e-03,
       5.40289771e-03, 5.38310810e-03, 5.37872144e-03, 5.35872012e-03,
       5.35184302e-03, 5.33878946e-03, 5.33393640e-03, 5.31894554e-03,
       5.31010576e-03, 5.30507620e-03, 5.29648981e-03, 5.28933966e-03,
       5.28485522e-03, 5.27883142e-03, 5.27565782e-03, 5.26373480e-03,
       5.26033154e-03, 5.25729926e-03, 5.25312928e-03, 5.24868684e-03,
       5.24605171e-03, 5.24126114e-03, 5.23897337e-03, 5.23118840e-03,
       5.22353904e-03, 5.21993000e-03, 5.21608779e-03, 5.20729314e-03,
       5.20251638e-03, 5.19510185e-03, 5.16457499e-03, 5.16157400e-03,
       5.14244425e-03, 5.12866758e-03, 5.11939350e-03, 5.08069289e-03,
       5.02244756e-03, 4.93287949e-03, 4.91140148e-03, 4.81989634e-03,
       4.77617964e-03, 4.71837765e-03, 4.65692458e-03, 4.59073089e-03,
       4.57488270e-03, 4.56299312e-03, 4.41088084e-03, 4.32589061e-03,
       4.30306148e-03, 4.13632318e-03, 4.07195448e-03, 4.06459621e-03,
       3.92877641e-03, 3.72067710e-03, 3.55941256e-03, 3.53151982e-03,
       3.43089001e-03, 3.29938574e-03, 3.19911861e-03, 3.13047650e-03,
       3.06330879e-03, 2.76707926e-03, 2.66756241e-03, 2.60812414e-03,
       2.38166802e-03, 2.31686654e-03, 2.28883541e-03, 2.28091842e-03,
       2.17156743e-03, 2.09457323e-03, 2.03862215e-03, 1.92772803e-03,
       1.83533103e-03, 1.79207407e-03, 1.64558258e-03, 1.59878225e-03,
       1.51489443e-03, 1.47956071e-03, 1.42273791e-03, 1.39128581e-03,
       1.36601345e-03, 1.30730061e-03, 1.24825036e-03, 1.18553824e-03,
       1.11581075e-03, 1.07269432e-03, 1.05420856e-03, 9.85063220e-04,
       9.51135631e-04, 9.12424952e-04, 8.95698262e-04, 8.84477986e-04,
       8.53732243e-04, 7.95435688e-04, 7.58056714e-04, 7.24294393e-04,
       6.86952880e-04, 6.44342070e-04, 6.14463485e-04, 5.73257608e-04,
       4.64617549e-04, 4.39061373e-04, 4.19671117e-04, 3.98208763e-04,
       3.74980040e-04, 2.70775777e-04, 1.08151745e-04, 8.37171131e-05,
       1.76195640e-05, 1.45053379e-05, 1.01668906e-29, 3.62530666e-30,
       2.26290486e-30, 1.94805775e-30, 1.94276729e-30, 1.73068278e-30,
       1.70662343e-30, 1.60808225e-30, 1.53000512e-30, 1.49374990e-30,
       1.18846679e-30, 1.00953430e-30, 9.85435835e-31, 9.37911291e-31,
       9.21061791e-31, 8.35511843e-31, 7.55477246e-31, 5.23199141e-31,
       5.09626608e-31, 4.68217464e-31, 4.22763413e-31, 3.21671068e-31,
       3.04777022e-31, 2.12598949e-31, 1.38260933e-31, 1.56097890e-32])
In [50]:
# Investigate the variance accounted for by each principal component.

def scree_plot(pca, components=None):
    '''
    Returns a scree plot of the principal components vs explained variance
    
    INPUT: pca - the result of instantian of PCA in scikit learn
            
    OUTPUT:
            None
    '''
    if components == None:
        components=len(pca.explained_variance_ratio_)
    index = np.arange(components)
    components_var = pca.explained_variance_ratio_[:components]
 
    plt.figure(figsize=(14, 12))
    ax = plt.subplot(211)
    cum_var = np.cumsum(components_var)
    ax.bar(index, components_var)
    ax.plot(index, cum_var)
 
    ax.set_xlabel("Principal Component")
    ax.set_ylabel("Explained Variance (%)")
    plt.title('Explained Variance by Principal Component')
In [51]:
scree_plot(pca)
No description has been provided for this image
In [52]:
#Lets interpret the chart above in words
for i in np.arange(10, len(pca.explained_variance_ratio_), 10):
    print('{} components explains {}% of variance'.format(i,(pca.explained_variance_ratio_[:i].sum()) * 100))

10 components explains 29.077258272872708% of variance
20 components explains 40.33834350011687% of variance
30 components explains 49.04624362894551% of variance
40 components explains 55.68688414369052% of variance
50 components explains 61.47158312318239% of variance
60 components explains 66.91264184959374% of variance
70 components explains 72.22346309879121% of variance
80 components explains 77.4750945158129% of variance
90 components explains 82.66126741687022% of variance
100 components explains 87.52415981884704% of variance
110 components explains 91.73416343294336% of variance
120 components explains 94.8598512176143% of variance
130 components explains 96.97266965105923% of variance
140 components explains 98.38866428518877% of variance
150 components explains 99.34073244707527% of variance
160 components explains 99.91302504223889% of variance
170 components explains 100.0% of variance
180 components explains 100.0% of variance
190 components explains 100.0% of variance
In [53]:
# Re-apply PCA to the data while selecting for number of components to retain.
pca = PCA(n_components=110)
pca_data = pca.fit_transform(scaled_df)

Discussion 2.2: Perform Dimensionality Reduction¶

(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding dimensionality reduction. How many principal components / transformed features are you retaining for the next step of the analysis?)

We can see that from 110 components, the increase in cumulative explained variance starts to reduce. 110 components explains about 92% of the variance and this seems good enough for me.

Step 2.3: Interpret Principal Components¶

Now that we have our transformed principal components, it's a nice idea to check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.

As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.

  • To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.
  • You should investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the i-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters.
In [58]:
# Map weights for the first principal component to corresponding feature names
# and then print the linked values, sorted by weight.
# HINT: Try defining a function here or in a new cell that you can reuse in the
# other cells.


def interpret_pca(data, pca, component_no, no_of_features):
    '''
    This function maps & visualizes the weight of a predefined Principal component for a prredefined no of features

    Input:
    data - The original dataframe which we performed pca on

    pca - the previously instantiated pca object

    component_no - the assigned no of the component which we want to visualize. i.e if we want the 5th component, component_no = 5 
    no_of_features - the no of features from the origian df which we want to visualize 
    '''
    
    comp = pd.DataFrame(np.round(pca.components_, 3), columns=data.keys()).iloc[component_no - 1]
    comp.sort_values(ascending=False, inplace=True)
    comp = pd.concat([comp.head(no_of_features), comp.tail(no_of_features)])
    
    comp.plot(kind='bar', 
                  title='Most {} weighted features for PCA component {}'.format(no_of_features*2, component_no),
                  figsize=(10, 6));
    plt.show()

    return comp
In [59]:
# Map weights for the first principal component to corresponding feature names
# and then print the linked values, sorted by weight.
interpret_pca(scaled_df, pca, 1, 10)
No description has been provided for this image
Out[59]:
LP_STATUS_GROB_1.0     0.197
HH_EINKOMMEN_SCORE     0.187
WEALTH                 0.187
PLZ8_ANTG3             0.183
PLZ8_ANTG4             0.177
ORTSGR_KLS9            0.155
EWDICHTE               0.154
FINANZ_HAUSBAUER       0.150
LP_STATUS_FEIN_1.0     0.128
KBA05_ANTG4            0.128
LP_STATUS_FEIN_10.0   -0.115
LP_STATUS_GROB_5.0    -0.115
INNENSTADT            -0.128
PLZ8_GBZ              -0.134
KONSUMNAEHE           -0.137
KBA05_GBZ             -0.182
PLZ8_ANTG1            -0.183
KBA05_ANTG1           -0.192
FINANZ_MINIMALIST     -0.192
MOBI_REGIO            -0.205
Name: 0, dtype: float64
In [60]:
# Map weights for the second principal component to corresponding feature names
# and then print the linked values, sorted by weight.
interpret_pca(scaled_df, pca, 2, 10)
No description has been provided for this image
Out[60]:
ALTERSKATEGORIE_GROB     0.233
FINANZ_VORSORGER         0.219
ZABEOTYP_3               0.200
SEMIO_ERL                0.180
SEMIO_LUST               0.164
RETOURTYP_BK_S           0.156
W_KEIT_KIND_HH           0.128
CJT_GESAMTTYP_2.0        0.108
FINANZTYP_5              0.098
LP_STATUS_FEIN_1.0       0.097
ONLINE_AFFINITAET       -0.163
SEMIO_KULT              -0.164
SEMIO_RAT               -0.168
FINANZ_ANLEGER          -0.203
SEMIO_PFLICHT           -0.205
SEMIO_TRADV             -0.208
SEMIO_REL               -0.214
FINANZ_UNAUFFAELLIGER   -0.216
FINANZ_SPARER           -0.227
GENERATION              -0.238
Name: 1, dtype: float64
In [61]:
# Map weights for the third principal component to corresponding feature names
# and then print the linked values, sorted by weight.
interpret_pca(scaled_df, pca, 3, 10)
No description has been provided for this image
Out[61]:
SEMIO_VERT            0.319
SEMIO_FAM             0.261
SEMIO_SOZ             0.257
SEMIO_KULT            0.252
FINANZTYP_5           0.135
FINANZ_MINIMALIST     0.129
SHOPPER_TYP_0.0       0.123
ZABEOTYP_1            0.117
SEMIO_REL             0.113
SEMIO_MAT             0.089
LP_STATUS_FEIN_2.0   -0.079
SHOPPER_TYP_2.0      -0.093
FINANZTYP_1          -0.100
FINANZ_ANLEGER       -0.153
SEMIO_RAT            -0.155
SEMIO_ERL            -0.208
SEMIO_KRIT           -0.267
SEMIO_DOM            -0.284
SEMIO_KAEM           -0.316
ANREDE_KZ            -0.346
Name: 2, dtype: float64

Discussion 2.3: Interpret Principal Components¶

(Double-click this cell and replace this text with your own text, reporting your observations from detailed investigation of the first few principal components generated. Can we interpret positive and negative values from them in a meaningful way?)

Step 3: Clustering¶

Step 3.1: Apply Clustering to General Population¶

You've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, you will apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.

  • Use sklearn's KMeans class to perform k-means clustering on the PCA-transformed data.
  • Then, compute the average difference from each point to its assigned cluster's center. Hint: The KMeans object's .score() method might be useful here, but note that in sklearn, scores tend to be defined so that larger is better. Try applying it to a small, toy dataset, or use an internet search to help your understanding.
  • Perform the above two steps for a number of different cluster counts. You can then see how the average distance decreases with an increasing number of clusters. However, each additional cluster provides a smaller net benefit. Use this fact to select a final number of clusters in which to group the data. Warning: because of the large size of the dataset, it can take a long time for the algorithm to resolve. The more clusters to fit, the longer the algorithm will take. You should test for cluster counts through at least 10 clusters to get the full picture, but you shouldn't need to test for a number of clusters above about 30.
  • Once you've selected a final number of clusters to use, re-fit a KMeans instance to perform the clustering operation. Make sure that you also obtain the cluster assignments for the general demographics data, since you'll be using them in the final Step 3.3.
In [65]:
# Over a number of different cluster counts...


    # run k-means clustering on the data and...
    
    
    # compute the average within-cluster distances.
    
def calc_kmeans_score(data, no_of_clusters, iterations=50):
    '''
    returns the kmeans score regarding SSE for points to centers
    INPUT:
        data - the dataframe you want to perform cluster analysis on
        no_of_clusters - the number of clusters you want
        iterations - the no of iterations for kmeans
    OUTPUT:
        score - the SSE score for the kmeans model fit to the data
    '''
    #instantiate kmeans
    kmeans = KMeans(n_clusters=no_of_clusters, max_iter=iterations) 

    # Then fit the model to your data using the fit method
    model = kmeans.fit(data)
    
    # Obtain a score related to the model fit
    score = np.abs(model.score(data))
    
    return score
In [66]:
# Investigate the change in within-cluster distance across number of clusters.
# HINT: Use matplotlib's plot function to visualize this relationship.

scores = []
no_clusters = np.arange(2,20)

for no in no_clusters:
    scores.append(calc_kmeans_score(pca_data, no))

plt.plot(no_clusters, scores, linestyle='--', marker='o', color='r');
plt.xlabel('No of clusters');
plt.ylabel('SSE');
plt.title('SSE vs. No of clusters')

/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
Out[66]:
Text(0.5, 1.0, 'SSE vs. No of clusters')
No description has been provided for this image
In [69]:
for num,val in zip(scores, no_clusters):
    print(f'{val} clusters - {num}')

2 clusters - 131144435.0217952
3 clusters - 126675395.72868861
4 clusters - 123707271.26927544
5 clusters - 121346190.77353124
6 clusters - 120020861.19235522
7 clusters - 118800708.08749482
8 clusters - 117093184.01650813
9 clusters - 116016375.34366006
10 clusters - 115041685.70079474
11 clusters - 114377406.73361064
12 clusters - 114041058.48741011
13 clusters - 112588938.19444002
14 clusters - 112299310.29008305
15 clusters - 111173790.05742827
16 clusters - 110691277.21576288
17 clusters - 110007638.4333015
18 clusters - 109470760.98109956
19 clusters - 109055587.93218184
In [157]:
# Re-fit the k-means model with the selected number of clusters and obtain
# cluster predictions for the general population demographics data.
# cluster predictions for the general population demographics data.
kmeans = KMeans(n_clusters=13)
model = kmeans.fit(pca_data)
prediction_pop = model.predict(pca_data)

/opt/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
In [158]:
prediction_pop_df = pd.DataFrame(prediction_pop, columns = ['cluster'])

Discussion 3.1: Apply Clustering to General Population¶

(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding clustering. Into how many clusters have you decided to segment the population?)

The scree plot shows that there is no much improvement in the score after K=13.Therefore, I have chosen k = 13 clusters

Step 3.2: Apply All Steps to the Customer Data¶

Now that you have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Take care to not confuse this for re-fitting all of the models to the customer data. Instead, you're going to use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, you will interpret how the general population fits apply to the customer data.

  • Don't forget when loading in the customers data, that it is semicolon (;) delimited.
  • Apply the same feature wrangling, selection, and engineering steps to the customer demographics using the clean_data() function you created earlier. (You can assume that the customer demographics data has similar meaning behind missing data patterns as the general demographics data.)
  • Use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, you should not be using a .fit() or .fit_transform() method to re-fit the old objects, nor should you be creating new sklearn objects! Carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data.
In [159]:
# Load in the customer demographics data.
customers = pd.read_csv('Udacity_CUSTOMERS_Subset.csv', sep=';')
In [160]:
customers.shape
Out[160]:
(191652, 85)
In [161]:
customers.head()
Out[161]:
AGER_TYP ALTERSKATEGORIE_GROB ANREDE_KZ CJT_GESAMTTYP FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER FINANZTYP GEBURTSJAHR GFK_URLAUBERTYP GREEN_AVANTGARDE HEALTH_TYP LP_LEBENSPHASE_FEIN LP_LEBENSPHASE_GROB LP_FAMILIE_FEIN LP_FAMILIE_GROB LP_STATUS_FEIN LP_STATUS_GROB NATIONALITAET_KZ PRAEGENDE_JUGENDJAHRE RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SHOPPER_TYP SOHO_KZ TITEL_KZ VERS_TYP ZABEOTYP ALTER_HH ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE KK_KUNDENTYP W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL GEBAEUDETYP KONSUMNAEHE MIN_GEBAEUDEJAHR OST_WEST_KZ WOHNLAGE CAMEO_DEUG_2015 CAMEO_DEU_2015 CAMEO_INTL_2015 KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_BAUMAX KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB
0 2 4 1 5.0 5 1 5 1 2 2 2 0 4.0 1 1 20.0 5.0 2.0 2.0 10.0 5.0 1 4 5.0 6 5 2 6 6 7 3 4 1 3 1 1 2 1 3 0.0 0.0 1 3 10.0 2.0 0.0 1.0 NaN 6.0 9.0 1.0 0.0 1.0 5.0 1992.0 W 7.0 1 1A 13 2.0 2.0 0.0 0.0 0.0 4.0 3.0 2.0 4.0 4.0 1.0 4.0 3.0 1.0 1201.0 3.0 3.0 1.0 0.0 1.0 5.0 5.0 1.0 2.0 1.0
1 -1 4 1 NaN 5 1 5 1 3 2 2 0 NaN 0 1 NaN NaN NaN NaN NaN NaN 1 0 NaN 3 6 2 6 7 5 3 4 1 3 3 2 4 1 3 0.0 0.0 1 3 11.0 3.0 0.0 NaN NaN 0.0 9.0 NaN NaN NaN 5.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 -1 4 2 2.0 5 1 5 1 4 4 2 0 3.0 1 2 13.0 3.0 1.0 1.0 10.0 5.0 1 4 5.0 2 2 1 3 3 7 7 1 2 7 5 6 4 1 1 0.0 0.0 2 3 6.0 1.0 0.0 1.0 NaN 6.0 9.0 1.0 0.0 8.0 1.0 1992.0 W 2.0 5 5D 34 2.0 2.0 0.0 0.0 0.0 3.0 7.0 4.0 1.0 3.0 3.0 3.0 1.0 7.0 433.0 2.0 3.0 3.0 1.0 3.0 3.0 2.0 3.0 5.0 3.0
3 1 4 1 2.0 5 1 5 2 1 2 6 0 10.0 0 2 0.0 0.0 0.0 0.0 9.0 4.0 1 1 3.0 6 5 3 4 7 5 3 4 3 3 3 3 3 4 0 0.0 0.0 1 1 8.0 0.0 0.0 4.0 NaN NaN 9.0 0.0 NaN 2.0 2.0 1992.0 W 7.0 4 4C 24 3.0 0.0 0.0 0.0 1.0 4.0 7.0 1.0 7.0 4.0 3.0 4.0 2.0 6.0 755.0 3.0 2.0 1.0 0.0 1.0 3.0 4.0 1.0 3.0 1.0
4 -1 3 1 6.0 3 1 4 4 5 2 2 1960 2.0 0 3 31.0 10.0 10.0 5.0 1.0 1.0 1 8 5.0 4 5 4 6 5 6 4 5 5 3 5 2 5 4 1 0.0 0.0 2 1 20.0 4.0 0.0 6.0 2.0 2.0 9.0 7.0 0.0 3.0 1.0 1992.0 W 3.0 7 7B 41 0.0 3.0 2.0 0.0 0.0 3.0 3.0 4.0 4.0 3.0 4.0 3.0 5.0 7.0 513.0 2.0 4.0 2.0 1.0 2.0 3.0 3.0 3.0 5.0 1.0
In [162]:
for col in customers.columns:
    print(col)

AGER_TYP
ALTERSKATEGORIE_GROB
ANREDE_KZ
CJT_GESAMTTYP
FINANZ_MINIMALIST
FINANZ_SPARER
FINANZ_VORSORGER
FINANZ_ANLEGER
FINANZ_UNAUFFAELLIGER
FINANZ_HAUSBAUER
FINANZTYP
GEBURTSJAHR
GFK_URLAUBERTYP
GREEN_AVANTGARDE
HEALTH_TYP
LP_LEBENSPHASE_FEIN
LP_LEBENSPHASE_GROB
LP_FAMILIE_FEIN
LP_FAMILIE_GROB
LP_STATUS_FEIN
LP_STATUS_GROB
NATIONALITAET_KZ
PRAEGENDE_JUGENDJAHRE
RETOURTYP_BK_S
SEMIO_SOZ
SEMIO_FAM
SEMIO_REL
SEMIO_MAT
SEMIO_VERT
SEMIO_LUST
SEMIO_ERL
SEMIO_KULT
SEMIO_RAT
SEMIO_KRIT
SEMIO_DOM
SEMIO_KAEM
SEMIO_PFLICHT
SEMIO_TRADV
SHOPPER_TYP
SOHO_KZ
TITEL_KZ
VERS_TYP
ZABEOTYP
ALTER_HH
ANZ_PERSONEN
ANZ_TITEL
HH_EINKOMMEN_SCORE
KK_KUNDENTYP
W_KEIT_KIND_HH
WOHNDAUER_2008
ANZ_HAUSHALTE_AKTIV
ANZ_HH_TITEL
GEBAEUDETYP
KONSUMNAEHE
MIN_GEBAEUDEJAHR
OST_WEST_KZ
WOHNLAGE
CAMEO_DEUG_2015
CAMEO_DEU_2015
CAMEO_INTL_2015
KBA05_ANTG1
KBA05_ANTG2
KBA05_ANTG3
KBA05_ANTG4
KBA05_BAUMAX
KBA05_GBZ
BALLRAUM
EWDICHTE
INNENSTADT
GEBAEUDETYP_RASTER
KKK
MOBI_REGIO
ONLINE_AFFINITAET
REGIOTYP
KBA13_ANZAHL_PKW
PLZ8_ANTG1
PLZ8_ANTG2
PLZ8_ANTG3
PLZ8_ANTG4
PLZ8_BAUMAX
PLZ8_HHZ
PLZ8_GBZ
ARBEIT
ORTSGR_KLS9
RELAT_AB
In [163]:
# Apply preprocessing, feature transformation, and clustering from the general
# demographics onto the customer data, obtaining cluster predictions for the
# customer demographics data.
cleaned_customers = clean_data(customers)
In [164]:
cleaned_customers.head()
Out[164]:
ALTERSKATEGORIE_GROB ANREDE_KZ FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER GREEN_AVANTGARDE HEALTH_TYP LP_LEBENSPHASE_FEIN LP_LEBENSPHASE_GROB RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SOHO_KZ VERS_TYP ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL KONSUMNAEHE MIN_GEBAEUDEJAHR OST_WEST_KZ WOHNLAGE KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB CJT_GESAMTTYP_1.0 CJT_GESAMTTYP_2.0 CJT_GESAMTTYP_3.0 CJT_GESAMTTYP_4.0 CJT_GESAMTTYP_5.0 CJT_GESAMTTYP_6.0 FINANZTYP_1 FINANZTYP_2 FINANZTYP_3 FINANZTYP_4 FINANZTYP_5 FINANZTYP_6 GFK_URLAUBERTYP_1.0 GFK_URLAUBERTYP_2.0 GFK_URLAUBERTYP_3.0 GFK_URLAUBERTYP_4.0 GFK_URLAUBERTYP_5.0 GFK_URLAUBERTYP_6.0 GFK_URLAUBERTYP_7.0 GFK_URLAUBERTYP_8.0 GFK_URLAUBERTYP_9.0 GFK_URLAUBERTYP_10.0 GFK_URLAUBERTYP_11.0 GFK_URLAUBERTYP_12.0 LP_FAMILIE_FEIN_1.0 LP_FAMILIE_FEIN_2.0 LP_FAMILIE_FEIN_3.0 LP_FAMILIE_FEIN_4.0 LP_FAMILIE_FEIN_5.0 LP_FAMILIE_FEIN_6.0 LP_FAMILIE_FEIN_7.0 LP_FAMILIE_FEIN_8.0 LP_FAMILIE_FEIN_9.0 LP_FAMILIE_FEIN_10.0 LP_FAMILIE_FEIN_11.0 LP_FAMILIE_GROB_1.0 LP_FAMILIE_GROB_2.0 LP_FAMILIE_GROB_3.0 LP_FAMILIE_GROB_4.0 LP_FAMILIE_GROB_5.0 LP_STATUS_FEIN_1.0 LP_STATUS_FEIN_2.0 LP_STATUS_FEIN_3.0 LP_STATUS_FEIN_4.0 LP_STATUS_FEIN_5.0 LP_STATUS_FEIN_6.0 LP_STATUS_FEIN_7.0 LP_STATUS_FEIN_8.0 LP_STATUS_FEIN_9.0 LP_STATUS_FEIN_10.0 LP_STATUS_GROB_1.0 LP_STATUS_GROB_2.0 LP_STATUS_GROB_3.0 LP_STATUS_GROB_4.0 LP_STATUS_GROB_5.0 NATIONALITAET_KZ_1.0 NATIONALITAET_KZ_2.0 NATIONALITAET_KZ_3.0 SHOPPER_TYP_0.0 SHOPPER_TYP_1.0 SHOPPER_TYP_2.0 SHOPPER_TYP_3.0 ZABEOTYP_1 ZABEOTYP_2 ZABEOTYP_3 ZABEOTYP_4 ZABEOTYP_5 ZABEOTYP_6 GEBAEUDETYP_1.0 GEBAEUDETYP_2.0 GEBAEUDETYP_3.0 GEBAEUDETYP_4.0 GEBAEUDETYP_6.0 GEBAEUDETYP_8.0 CAMEO_DEUG_2015_1 CAMEO_DEUG_2015_2 CAMEO_DEUG_2015_3 CAMEO_DEUG_2015_4 CAMEO_DEUG_2015_5 CAMEO_DEUG_2015_6 CAMEO_DEUG_2015_7 CAMEO_DEUG_2015_8 CAMEO_DEUG_2015_9 CAMEO_DEU_2015_1A CAMEO_DEU_2015_1B CAMEO_DEU_2015_1C CAMEO_DEU_2015_1D CAMEO_DEU_2015_1E CAMEO_DEU_2015_2A CAMEO_DEU_2015_2B CAMEO_DEU_2015_2C CAMEO_DEU_2015_2D CAMEO_DEU_2015_3A CAMEO_DEU_2015_3B CAMEO_DEU_2015_3C CAMEO_DEU_2015_3D CAMEO_DEU_2015_4A CAMEO_DEU_2015_4B CAMEO_DEU_2015_4C CAMEO_DEU_2015_4D CAMEO_DEU_2015_4E CAMEO_DEU_2015_5A CAMEO_DEU_2015_5B CAMEO_DEU_2015_5C CAMEO_DEU_2015_5D CAMEO_DEU_2015_5E CAMEO_DEU_2015_5F CAMEO_DEU_2015_6A CAMEO_DEU_2015_6B CAMEO_DEU_2015_6C CAMEO_DEU_2015_6D CAMEO_DEU_2015_6E CAMEO_DEU_2015_6F CAMEO_DEU_2015_7A CAMEO_DEU_2015_7B CAMEO_DEU_2015_7C CAMEO_DEU_2015_7D CAMEO_DEU_2015_7E CAMEO_DEU_2015_8A CAMEO_DEU_2015_8B CAMEO_DEU_2015_8C CAMEO_DEU_2015_8D CAMEO_DEU_2015_9A CAMEO_DEU_2015_9B CAMEO_DEU_2015_9C CAMEO_DEU_2015_9D CAMEO_DEU_2015_9E GENERATION MOVEMENT WEALTH LIFE_STAGE
0 4.0 0 5 1 5 1 2 2 1 1.0 20.0 5.0 5.0 6 5 2 6 6 7 3 4 1 3 1 1 2 1 0.0 1.0 2.0 0.0 1.0 6.0 9.0 1.0 0.0 5.0 1992.0 1 7.0 2.0 2.0 0.0 0.0 4.0 3.0 2.0 4.0 4.0 1.0 4.0 3.0 1.0 1201.0 3.0 3.0 1.0 0.0 1.0 5.0 5.0 1.0 2.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 1.0 1 3
2 4.0 1 5 1 5 1 4 4 1 2.0 13.0 3.0 5.0 2 2 1 3 3 7 7 1 2 7 5 6 4 1 0.0 2.0 1.0 0.0 1.0 6.0 9.0 1.0 0.0 1.0 1992.0 1 2.0 2.0 2.0 0.0 0.0 3.0 7.0 4.0 1.0 3.0 3.0 3.0 1.0 7.0 433.0 2.0 3.0 3.0 1.0 3.0 3.0 2.0 3.0 5.0 3.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 1.0 3 4
3 4.0 0 5 1 5 2 1 2 0 2.0 NaN NaN 3.0 6 5 3 4 7 5 3 4 3 3 3 3 3 4 0.0 1.0 0.0 0.0 4.0 NaN 9.0 NaN NaN 2.0 1992.0 1 7.0 3.0 0.0 0.0 0.0 4.0 7.0 1.0 7.0 4.0 3.0 4.0 2.0 6.0 755.0 3.0 2.0 1.0 0.0 1.0 3.0 4.0 1.0 3.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 40.0 0.0 2 4
4 3.0 0 3 1 4 4 5 2 0 3.0 31.0 10.0 5.0 4 5 4 6 5 6 4 5 5 3 5 2 5 4 0.0 2.0 4.0 0.0 6.0 2.0 9.0 7.0 0.0 1.0 1992.0 1 3.0 0.0 3.0 2.0 0.0 3.0 3.0 4.0 4.0 3.0 4.0 3.0 5.0 7.0 513.0 2.0 4.0 2.0 1.0 2.0 3.0 3.0 3.0 5.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 70.0 0.0 4 1
5 3.0 0 5 1 5 1 2 3 1 3.0 17.0 5.0 3.0 6 4 4 1 7 6 4 6 2 5 5 3 3 4 0.0 2.0 2.0 0.0 1.0 6.0 9.0 1.0 0.0 2.0 1992.0 1 1.0 2.0 2.0 1.0 0.0 3.0 7.0 5.0 8.0 4.0 2.0 3.0 3.0 3.0 1167.0 2.0 3.0 2.0 1.0 1.0 5.0 5.0 3.0 7.0 5.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 1.0 3 4
In [165]:
cleaned_customers.shape
Out[165]:
(141701, 195)
In [166]:
# Check how many rows (if any) have missing values
cleaned_customers.isna().any().sum()
Out[166]:
37
In [167]:
#let's remove these rows 
cleaned_customers.dropna(inplace=True)

The 'GEBAEUDETYP_5.0' column is missing from the customers dataset. lets add this col to the dataset and set all values to 0

In [168]:
cleaned_customers['GEBAEUDETYP_5.0']=0

Reorder the columns in customers dataset to be the same as population data

In [169]:
cleaned_customers = cleaned_customers.reindex(columns=cleaned_data.columns)
In [170]:
cleaned_customers.head()
Out[170]:
ALTERSKATEGORIE_GROB ANREDE_KZ FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER GREEN_AVANTGARDE HEALTH_TYP RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SOHO_KZ VERS_TYP ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL KONSUMNAEHE MIN_GEBAEUDEJAHR OST_WEST_KZ KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB CJT_GESAMTTYP_1.0 CJT_GESAMTTYP_2.0 CJT_GESAMTTYP_3.0 CJT_GESAMTTYP_4.0 CJT_GESAMTTYP_5.0 CJT_GESAMTTYP_6.0 FINANZTYP_1 FINANZTYP_2 FINANZTYP_3 FINANZTYP_4 FINANZTYP_5 FINANZTYP_6 GFK_URLAUBERTYP_1.0 GFK_URLAUBERTYP_2.0 GFK_URLAUBERTYP_3.0 GFK_URLAUBERTYP_4.0 GFK_URLAUBERTYP_5.0 GFK_URLAUBERTYP_6.0 GFK_URLAUBERTYP_7.0 GFK_URLAUBERTYP_8.0 GFK_URLAUBERTYP_9.0 GFK_URLAUBERTYP_10.0 GFK_URLAUBERTYP_11.0 GFK_URLAUBERTYP_12.0 LP_FAMILIE_FEIN_1.0 LP_FAMILIE_FEIN_2.0 LP_FAMILIE_FEIN_3.0 LP_FAMILIE_FEIN_4.0 LP_FAMILIE_FEIN_5.0 LP_FAMILIE_FEIN_6.0 LP_FAMILIE_FEIN_7.0 LP_FAMILIE_FEIN_8.0 LP_FAMILIE_FEIN_9.0 LP_FAMILIE_FEIN_10.0 LP_FAMILIE_FEIN_11.0 LP_FAMILIE_GROB_1.0 LP_FAMILIE_GROB_2.0 LP_FAMILIE_GROB_3.0 LP_FAMILIE_GROB_4.0 LP_FAMILIE_GROB_5.0 LP_STATUS_FEIN_1.0 LP_STATUS_FEIN_2.0 LP_STATUS_FEIN_3.0 LP_STATUS_FEIN_4.0 LP_STATUS_FEIN_5.0 LP_STATUS_FEIN_6.0 LP_STATUS_FEIN_7.0 LP_STATUS_FEIN_8.0 LP_STATUS_FEIN_9.0 LP_STATUS_FEIN_10.0 LP_STATUS_GROB_1.0 LP_STATUS_GROB_2.0 LP_STATUS_GROB_3.0 LP_STATUS_GROB_4.0 LP_STATUS_GROB_5.0 NATIONALITAET_KZ_1.0 NATIONALITAET_KZ_2.0 NATIONALITAET_KZ_3.0 SHOPPER_TYP_0.0 SHOPPER_TYP_1.0 SHOPPER_TYP_2.0 SHOPPER_TYP_3.0 ZABEOTYP_1 ZABEOTYP_2 ZABEOTYP_3 ZABEOTYP_4 ZABEOTYP_5 ZABEOTYP_6 GEBAEUDETYP_1.0 GEBAEUDETYP_2.0 GEBAEUDETYP_3.0 GEBAEUDETYP_4.0 GEBAEUDETYP_5.0 GEBAEUDETYP_6.0 GEBAEUDETYP_8.0 CAMEO_DEUG_2015_1 CAMEO_DEUG_2015_2 CAMEO_DEUG_2015_3 CAMEO_DEUG_2015_4 CAMEO_DEUG_2015_5 CAMEO_DEUG_2015_6 CAMEO_DEUG_2015_7 CAMEO_DEUG_2015_8 CAMEO_DEUG_2015_9 CAMEO_DEU_2015_1A CAMEO_DEU_2015_1B CAMEO_DEU_2015_1C CAMEO_DEU_2015_1D CAMEO_DEU_2015_1E CAMEO_DEU_2015_2A CAMEO_DEU_2015_2B CAMEO_DEU_2015_2C CAMEO_DEU_2015_2D CAMEO_DEU_2015_3A CAMEO_DEU_2015_3B CAMEO_DEU_2015_3C CAMEO_DEU_2015_3D CAMEO_DEU_2015_4A CAMEO_DEU_2015_4B CAMEO_DEU_2015_4C CAMEO_DEU_2015_4D CAMEO_DEU_2015_4E CAMEO_DEU_2015_5A CAMEO_DEU_2015_5B CAMEO_DEU_2015_5C CAMEO_DEU_2015_5D CAMEO_DEU_2015_5E CAMEO_DEU_2015_5F CAMEO_DEU_2015_6A CAMEO_DEU_2015_6B CAMEO_DEU_2015_6C CAMEO_DEU_2015_6D CAMEO_DEU_2015_6E CAMEO_DEU_2015_6F CAMEO_DEU_2015_7A CAMEO_DEU_2015_7B CAMEO_DEU_2015_7C CAMEO_DEU_2015_7D CAMEO_DEU_2015_7E CAMEO_DEU_2015_8A CAMEO_DEU_2015_8B CAMEO_DEU_2015_8C CAMEO_DEU_2015_8D CAMEO_DEU_2015_9A CAMEO_DEU_2015_9B CAMEO_DEU_2015_9C CAMEO_DEU_2015_9D CAMEO_DEU_2015_9E GENERATION MOVEMENT WEALTH LIFE_STAGE
0 4.0 0 5 1 5 1 2 2 1 1.0 5.0 6 5 2 6 6 7 3 4 1 3 1 1 2 1 0.0 1.0 2.0 0.0 1.0 6.0 9.0 1.0 0.0 5.0 1992.0 1 2.0 2.0 0.0 0.0 4.0 3.0 2.0 4.0 4.0 1.0 4.0 3.0 1.0 1201.0 3.0 3.0 1.0 0.0 5.0 5.0 1.0 2.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 1.0 1 3
2 4.0 1 5 1 5 1 4 4 1 2.0 5.0 2 2 1 3 3 7 7 1 2 7 5 6 4 1 0.0 2.0 1.0 0.0 1.0 6.0 9.0 1.0 0.0 1.0 1992.0 1 2.0 2.0 0.0 0.0 3.0 7.0 4.0 1.0 3.0 3.0 3.0 1.0 7.0 433.0 2.0 3.0 3.0 1.0 3.0 2.0 3.0 5.0 3.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 1.0 3 4
4 3.0 0 3 1 4 4 5 2 0 3.0 5.0 4 5 4 6 5 6 4 5 5 3 5 2 5 4 0.0 2.0 4.0 0.0 6.0 2.0 9.0 7.0 0.0 1.0 1992.0 1 0.0 3.0 2.0 0.0 3.0 3.0 4.0 4.0 3.0 4.0 3.0 5.0 7.0 513.0 2.0 4.0 2.0 1.0 3.0 3.0 3.0 5.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 70.0 0.0 4 1
5 3.0 0 5 1 5 1 2 3 1 3.0 3.0 6 4 4 1 7 6 4 6 2 5 5 3 3 4 0.0 2.0 2.0 0.0 1.0 6.0 9.0 1.0 0.0 2.0 1992.0 1 2.0 2.0 1.0 0.0 3.0 7.0 5.0 8.0 4.0 2.0 3.0 3.0 3.0 1167.0 2.0 3.0 2.0 1.0 5.0 5.0 3.0 7.0 5.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 1.0 3 4
6 4.0 0 5 1 5 1 1 2 1 2.0 5.0 4 2 5 1 6 5 3 4 3 3 1 2 2 4 0.0 1.0 2.0 0.0 2.0 6.0 9.0 1.0 0.0 4.0 1992.0 1 4.0 1.0 0.0 0.0 4.0 6.0 2.0 5.0 4.0 2.0 4.0 4.0 3.0 1300.0 3.0 2.0 1.0 0.0 5.0 5.0 2.0 3.0 2.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 1.0 2 3
In [171]:
# Standardize the data witht he scaler that was fit on the train set 
scaled_customers = scaler.transform(cleaned_customers)
In [172]:
# Apply PCA to customer data
customers_pca = pca.transform(scaled_customers)

/opt/anaconda3/lib/python3.11/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names
  warnings.warn(
In [173]:
# Cluster predictions for customer data 
customers_clustered = model.predict(customers_pca)
In [174]:
customers_clustered_df = pd.DataFrame(customers_clustered, columns = ['cluster'])
customers_clustered_df
Out[174]:
cluster
0 12
1 12
2 10
3 11
4 12
5 12
6 12
7 12
8 4
9 0
10 12
11 12
12 12
13 5
14 3
15 10
16 3
17 10
18 3
19 4
20 3
21 11
22 0
23 1
24 12
25 11
26 0
27 12
28 12
29 12
30 3
31 1
32 12
33 6
34 9
35 12
36 11
37 3
38 3
39 2
40 11
41 3
42 12
43 3
44 11
45 2
46 0
47 4
48 12
49 1
50 11
51 12
52 3
53 3
54 11
55 3
56 1
57 3
58 3
59 12
60 1
61 12
62 11
63 11
64 10
65 0
66 12
67 12
68 8
69 3
70 11
71 11
72 3
73 10
74 11
75 4
76 1
77 12
78 1
79 12
80 10
81 11
82 2
83 12
84 11
85 12
86 5
87 12
88 8
89 11
90 11
91 12
92 4
93 12
94 12
95 11
96 10
97 3
98 3
99 12
100 12
101 4
102 10
103 12
104 1
105 12
106 3
107 3
108 12
109 12
110 12
111 8
112 3
113 1
114 3
115 12
116 12
117 3
118 10
119 11
120 11
121 4
122 12
123 3
124 0
125 3
126 3
127 11
128 1
129 10
130 12
131 0
132 12
133 3
134 12
135 3
136 12
137 12
138 0
139 12
140 12
141 12
142 12
143 12
144 10
145 8
146 12
147 3
148 12
149 11
150 12
151 12
152 6
153 3
154 11
155 10
156 12
157 3
158 12
159 1
160 11
161 0
162 1
163 12
164 12
165 4
166 11
167 10
168 3
169 12
170 3
171 12
172 3
173 8
174 10
175 12
176 3
177 12
178 12
179 10
180 6
181 12
182 3
183 12
184 12
185 12
186 3
187 1
188 6
189 12
190 12
191 1
192 3
193 10
194 12
195 12
196 12
197 12
198 12
199 12
200 10
201 1
202 3
203 8
204 12
205 10
206 1
207 12
208 0
209 1
210 0
211 3
212 11
213 12
214 12
215 4
216 1
217 12
218 3
219 12
220 3
221 0
222 6
223 3
224 1
225 1
226 12
227 0
228 12
229 11
230 11
231 10
232 3
233 12
234 3
235 12
236 8
237 3
238 12
239 0
240 11
241 0
242 8
243 12
244 3
245 3
246 12
247 12
248 0
249 0
250 9
251 3
252 5
253 11
254 11
255 1
256 12
257 12
258 3
259 12
260 11
261 12
262 3
263 12
264 12
265 12
266 12
267 12
268 0
269 4
270 12
271 1
272 12
273 12
274 1
275 3
276 12
277 0
278 10
279 1
280 1
281 12
282 11
283 9
284 12
285 5
286 11
287 4
288 0
289 12
290 3
291 12
292 1
293 12
294 3
295 10
296 0
297 12
298 11
299 12
300 11
301 3
302 11
303 11
304 2
305 3
306 10
307 12
308 12
309 12
310 0
311 1
312 12
313 3
314 11
315 3
316 1
317 9
318 11
319 12
320 1
321 7
322 10
323 10
324 3
325 1
326 12
327 3
328 1
329 3
330 12
331 5
332 10
333 12
334 3
335 12
336 12
337 4
338 0
339 12
340 6
341 1
342 1
343 12
344 3
345 1
346 12
347 0
348 11
349 11
350 1
351 4
352 4
353 0
354 11
355 10
356 12
357 12
358 12
359 3
360 12
361 12
362 3
363 10
364 8
365 12
366 12
367 3
368 10
369 3
370 1
371 12
372 10
373 3
374 1
375 12
376 4
377 12
378 11
379 12
380 12
381 12
382 12
383 3
384 12
385 3
386 12
387 2
388 12
389 0
390 4
391 11
392 12
393 1
394 12
395 12
396 0
397 1
398 12
399 8
400 3
401 11
402 11
403 3
404 12
405 12
406 12
407 3
408 3
409 3
410 6
411 12
412 3
413 12
414 11
415 3
416 3
417 2
418 12
419 3
420 11
421 11
422 12
423 12
424 11
425 10
426 1
427 12
428 0
429 3
430 12
431 12
432 1
433 12
434 3
435 12
436 12
437 0
438 11
439 3
440 11
441 12
442 1
443 9
444 11
445 0
446 12
447 8
448 0
449 12
450 11
451 12
452 11
453 0
454 12
455 4
456 1
457 1
458 10
459 12
460 3
461 3
462 12
463 8
464 3
465 12
466 11
467 12
468 12
469 11
470 12
471 3
472 4
473 0
474 6
475 12
476 12
477 3
478 4
479 12
480 12
481 3
482 3
483 11
484 1
485 12
486 10
487 3
488 0
489 12
490 12
491 12
492 3
493 10
494 3
495 1
496 12
497 0
498 12
499 8
500 3
501 12
502 12
503 12
504 4
505 10
506 6
507 11
508 12
509 12
510 3
511 0
512 12
513 3
514 12
515 10
516 10
517 3
518 3
519 12
520 12
521 11
522 1
523 12
524 10
525 6
526 3
527 11
528 1
529 10
530 3
531 0
532 9
533 12
534 10
535 11
536 12
537 12
538 3
539 12
540 12
541 11
542 12
543 3
544 11
545 3
546 4
547 11
548 12
549 3
550 11
551 12
552 12
553 12
554 1
555 11
556 12
557 3
558 11
559 11
560 12
561 12
562 11
563 3
564 12
565 0
566 12
567 12
568 12
569 10
570 10
571 11
572 12
573 3
574 11
575 12
576 1
577 12
578 3
579 12
580 11
581 12
582 11
583 1
584 12
585 11
586 10
587 1
588 3
589 12
590 12
591 8
592 12
593 12
594 10
595 11
596 12
597 12
598 12
599 11
600 12
601 12
602 1
603 12
604 3
605 11
606 12
607 10
608 11
609 3
610 3
611 12
612 8
613 12
614 3
615 11
616 12
617 11
618 2
619 3
620 4
621 12
622 10
623 12
624 11
625 12
626 12
627 0
628 9
629 10
630 12
631 0
632 3
633 3
634 3
635 12
636 12
637 12
638 4
639 10
640 12
641 12
642 4
643 10
644 11
645 12
646 0
647 10
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649 10
650 3
651 12
652 12
653 12
654 3
655 3
656 6
657 3
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659 3
660 1
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662 11
663 3
664 10
665 12
666 12
667 3
668 12
669 1
670 12
671 2
672 1
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674 1
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676 11
677 1
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679 12
680 12
681 3
682 12
683 0
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685 3
686 4
687 3
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689 1
690 3
691 0
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696 0
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699 1
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701 12
702 0
703 12
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705 1
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708 1
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710 10
711 12
712 12
713 12
714 12
715 12
716 3
717 3
718 12
719 3
720 12
721 12
722 12
723 11
724 12
725 3
726 12
727 12
728 11
729 11
730 12
731 3
732 12
733 1
734 12
735 6
736 11
737 4
738 12
739 11
740 10
741 12
742 0
743 4
744 11
745 1
746 4
747 3
748 12
749 3
750 11
751 3
752 11
753 12
754 3
755 3
756 3
757 12
758 11
759 12
760 4
761 3
762 3
763 12
764 6
765 12
766 11
767 1
768 12
769 12
770 12
771 12
772 1
773 1
774 12
775 3
776 12
777 12
778 3
779 12
780 0
781 12
782 3
783 12
784 11
785 12
786 7
787 11
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790 4
791 12
792 3
793 1
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795 12
796 12
797 7
798 4
799 0
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801 11
802 3
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804 12
805 12
806 12
807 11
808 0
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810 12
811 12
812 3
813 3
814 3
815 1
816 12
817 1
818 12
819 10
820 11
821 12
822 0
823 12
824 1
825 11
826 7
827 3
828 3
829 3
830 2
831 3
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833 3
834 10
835 0
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837 11
838 12
839 12
840 12
841 11
842 12
843 12
844 10
845 12
846 0
847 3
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849 11
850 12
851 8
852 12
853 1
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855 12
856 12
857 1
858 0
859 9
860 1
861 12
862 3
863 12
864 12
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115570 12
115571 10
115572 12
115573 10
115574 3
115575 12
115576 3
115577 11
115578 0
115579 12
115580 3
115581 2
115582 12
115583 1
115584 11
115585 3
115586 9
115587 1
115588 3
115589 12
115590 10
115591 0
115592 12
115593 1
115594 4
115595 3
115596 12
115597 12
115598 12
115599 12
115600 11
115601 2
115602 1
115603 12
115604 12
115605 12
115606 1
115607 12
115608 10
115609 2
115610 12
115611 10
115612 1
115613 11
115614 1
115615 1
115616 3
115617 12
115618 12
115619 12
115620 10
115621 0
115622 12
115623 0
115624 11
115625 3
115626 12
115627 3
115628 3
115629 11
115630 12
115631 10
115632 11
115633 12
115634 0
115635 12
115636 1
115637 11
115638 0
115639 11
115640 12
115641 1
115642 3

Step 3.3: Compare Customer Data to Demographics Data¶

At this point, you have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, you will compare the two cluster distributions to see where the strongest customer base for the company is.

Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.

Take a look at the following points in this step:

  • Compute the proportion of data points in each cluster for the general population and the customer data. Visualizations will be useful here: both for the individual dataset proportions, but also to visualize the ratios in cluster representation between groups. Seaborn's countplot() or barplot() function could be handy.
    • Recall the analysis you performed in step 1.1.3 of the project, where you separated out certain data points from the dataset if they had more than a specified threshold of missing values. If you found that this group was qualitatively different from the main bulk of the data, you should treat this as an additional data cluster in this analysis. Make sure that you account for the number of data points in this subset, for both the general population and customer datasets, when making your computations!
  • Which cluster or clusters are overrepresented in the customer dataset compared to the general population? Select at least one such cluster and infer what kind of people might be represented by that cluster. Use the principal component interpretations from step 2.3 or look at additional components to help you make this inference. Alternatively, you can use the .inverse_transform() method of the PCA and StandardScaler objects to transform centroids back to the original data space and interpret the retrieved values directly.
  • Perform a similar investigation for the underrepresented clusters. Which cluster or clusters are underrepresented in the customer dataset compared to the general population, and what kinds of people are typified by these clusters?
In [182]:
population_proportion = []
customers_proportion = []
clusters = np.arange(0, 13)

for cluster in clusters:
    population_proportion.append((prediction_pop_df['cluster'] == cluster).sum() * 100 / len(prediction_pop_df))
    customers_proportion.append((customers_clustered_df['cluster'] == cluster).sum() * 100 / len(customers_clustered_df))

final_df = pd.DataFrame({'cluster': clusters, 'population': population_proportion, 'customers':customers_proportion})
final_df
Out[182]:
cluster population customers
0 0 9.887004 6.354038
1 1 6.526709 9.394429
2 2 1.654456 1.896353
3 3 12.924079 18.543275
4 4 4.456415 3.291163
5 5 8.336947 0.665842
6 6 9.579825 1.114637
7 7 12.462997 0.819764
8 8 6.055475 3.151942
9 9 2.281597 0.261149
10 10 10.063717 6.411110
11 11 4.308778 11.636675
12 12 11.462002 36.459621
In [183]:
# Compare the proportion of data in each cluster for the customer data to the
# proportion of data in each cluster for the general population.

melted_df = final_df.melt(id_vars='cluster', value_vars=['population', 'customers'], var_name='Type', value_name='Proportion')

# Create the bar plot
plt.figure(figsize=(10, 6))
sns.barplot(x='cluster', y='Proportion', hue='Type', data=melted_df)
plt.title('Proportion of Population and Customers by Cluster')
plt.xlabel('Cluster')
plt.ylabel('Proportion (%)')
plt.legend(title='Type')
plt.show()
No description has been provided for this image
In [ ]:
# What kinds of people are part of a cluster that is overrepresented in the
# customer data compared to the general population?

cluster 11 is over represented in the customers data

In [186]:
centroid_cluster_11 = pd.Series(kmeans.cluster_centers_[11]).sort_values(ascending=False)
centroid_cluster_11
Out[186]:
6      4.343530
5      2.481202
3      1.574536
12     1.423640
13     1.328228
23     1.278563
14     1.250230
8      1.067814
2      1.034110
20     0.988462
21     0.572984
16     0.409500
4      0.379973
24     0.370721
19     0.275169
42     0.256418
26     0.252578
38     0.222391
93     0.220949
90     0.185488
47     0.179696
46     0.154131
105    0.151055
78     0.145334
17     0.106698
98     0.100386
67     0.099058
25     0.094794
95     0.091758
62     0.085924
45     0.084304
77     0.077502
63     0.075634
72     0.074930
85     0.070107
89     0.068081
31     0.063203
73     0.049487
58     0.047727
106    0.046008
74     0.038606
68     0.037527
71     0.034429
96     0.034338
81     0.033640
9      0.030459
36     0.025606
76     0.018513
83     0.017785
66     0.016744
79     0.016657
87     0.016183
107    0.015406
64     0.010797
91     0.007181
69     0.005357
1     -0.000613
56    -0.005171
51    -0.007753
80    -0.014567
59    -0.022806
86    -0.024104
75    -0.026552
60    -0.028969
54    -0.028983
84    -0.030987
101   -0.034625
40    -0.041559
70    -0.045410
57    -0.057189
82    -0.059028
65    -0.061260
43    -0.062738
37    -0.064747
88    -0.067075
61    -0.073194
109   -0.079066
49    -0.080552
29    -0.080956
103   -0.082918
55    -0.104314
39    -0.124283
108   -0.139143
32    -0.146870
94    -0.157440
34    -0.158533
53    -0.175473
30    -0.176531
92    -0.178231
97    -0.181004
52    -0.185021
35    -0.196970
22    -0.205534
41    -0.215132
99    -0.232613
100   -0.259836
50    -0.262639
44    -0.275234
104   -0.290178
27    -0.295162
11    -0.296700
48    -0.324971
102   -0.402835
28    -0.476216
7     -0.563324
33    -0.583699
15    -0.763600
18    -1.672423
10    -2.106529
0     -2.114854
dtype: float64
In [187]:
#pca component 6 is the highest. now lets investigate what features are highlighted in this component
In [188]:
interpret_pca(cleaned_customers, pca, 6, 5)
No description has been provided for this image
Out[188]:
LP_STATUS_GROB_2.0     0.328
LP_STATUS_FEIN_4.0     0.240
FINANZ_HAUSBAUER       0.207
LP_FAMILIE_GROB_5.0    0.194
LP_STATUS_GROB_3.0     0.173
LP_STATUS_FEIN_10.0   -0.152
LP_STATUS_GROB_5.0    -0.152
FINANZTYP_3           -0.154
LP_STATUS_FEIN_9.0    -0.168
LP_STATUS_GROB_4.0    -0.205
Name: 5, dtype: float64

LP_STATUS_GROB_2.0 - social status - average earners LP_STATUS_FEIN_4.0 - social status - villagers FINANZ_HAUSBAUER - financial topology LP_FAMILIE_GROB_5.0 - multiperson household LP_STATUS_GROB_3.0 - social status- independents LP_STATUS_FEIN_10.0 - top earners LP_STATUS_GROB_5.0 - social status - top earners FINANZTYP_3 - home ownership LP_STATUS_FEIN_9.0 - houseowners LP_STATUS_GROB_4.0 - social status - top earners

As indicated above, the people in this cluster are mostly average earners with low finiancial and social status

In [ ]:
# What kinds of people are part of a cluster that is underrepresented in the
# customer data compared to the general population?

cluster 6 is under represented in the customers data

In [192]:
centroid_cluster_6 = pd.Series(kmeans.cluster_centers_[6]).sort_values(ascending=False)
centroid_cluster_6
Out[192]:
5      1.035129
25     0.430840
31     0.408673
24     0.384904
29     0.376951
36     0.343028
21     0.330644
8      0.298130
40     0.228772
18     0.187005
10     0.183976
32     0.182273
96     0.173790
48     0.163050
11     0.161507
15     0.151656
72     0.116179
17     0.112360
95     0.102006
47     0.099727
106    0.098821
68     0.089518
98     0.082812
71     0.080046
102    0.070843
39     0.069168
79     0.064596
37     0.062890
44     0.061338
108    0.060605
100    0.055232
78     0.054297
51     0.050828
34     0.044013
56     0.034174
62     0.032194
69     0.031271
67     0.025738
54     0.025462
60     0.024966
94     0.023821
77     0.023191
80     0.022712
104    0.021004
83     0.019394
103    0.017498
88     0.016926
99    -0.000716
73    -0.003554
49    -0.003726
90    -0.004211
76    -0.005065
61    -0.005654
63    -0.006746
52    -0.006961
58    -0.010417
93    -0.012198
66    -0.014522
65    -0.017203
84    -0.017442
57    -0.020854
87    -0.022903
75    -0.025597
74    -0.026451
82    -0.026887
45    -0.027072
20    -0.028605
86    -0.030709
53    -0.031275
91    -0.032182
70    -0.035915
81    -0.038332
85    -0.040556
55    -0.053808
97    -0.057979
35    -0.060524
59    -0.065154
89    -0.069415
50    -0.070693
107   -0.071602
64    -0.076296
23    -0.077492
46    -0.080148
28    -0.103629
109   -0.106749
92    -0.107827
105   -0.111691
43    -0.131380
9     -0.131587
7     -0.132312
101   -0.147045
33    -0.161341
30    -0.170109
42    -0.182641
22    -0.184476
26    -0.198347
41    -0.208758
27    -0.230511
16    -0.234530
13    -0.249569
38    -0.390655
12    -0.408198
19    -0.445346
6     -0.462507
14    -0.556442
4     -0.626419
3     -0.628211
2     -0.862589
0     -1.395443
1     -4.435963
dtype: float64
In [190]:
#pca component 0 is the highest. Now lets investigate what features are highlighted in this component
In [193]:
interpret_pca(cleaned_customers, pca, 5, 5)
No description has been provided for this image
Out[193]:
ANZ_PERSONEN            0.293
LP_FAMILIE_GROB_5.0     0.264
LP_FAMILIE_FEIN_10.0    0.201
KKK                     0.182
REGIOTYP                0.143
MOVEMENT               -0.124
LP_STATUS_GROB_2.0     -0.148
W_KEIT_KIND_HH         -0.233
LP_FAMILIE_GROB_1.0    -0.340
LP_FAMILIE_FEIN_1.0    -0.340
Name: 4, dtype: float64

LP_FAMILIE_GROB_5.0 multiperson household LP_FAMILIE_FEIN_10.0 two-generational household LP_STATUS_GROB_2.0 average earners LP_FAMILIE_GROB_1.0 single LP_FAMILIE_FEIN_1.0 typical low-income earners

Discussion 3.3: Compare Customer Data to Demographics Data¶

(Double-click this cell and replace this text with your own text, reporting findings and conclusions from the clustering analysis. Can we describe segments of the population that are relatively popular with the mail-order company, or relatively unpopular with the company?)

segments of people popular in the mail order company - average earners with low/average financial status, non-houseowners segments of people unpopular in the mail order company - House owners, multi person households.

Congratulations on making it this far in the project! Before you finish, make sure to check through the entire notebook from top to bottom to make sure that your analysis follows a logical flow and all of your findings are documented in Discussion cells. Once you've checked over all of your work, you should export the notebook as an HTML document to submit for evaluation. You can do this from the menu, navigating to File -> Download as -> HTML (.html). You will submit both that document and this notebook for your project submission.

In [ ]: